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https://github.com/zubax/jafit

Jiles-Atherton system identification tool: Given a B(H) curve, finds the Jiles-Atherton model coefficients. Supports various JA model formulations. Has an interactive GUI.
https://github.com/zubax/jafit

hysteresis-loop jiles-atherton magnet magnetism model-fitting system-identification

Last synced: 7 months ago
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Jiles-Atherton system identification tool: Given a B(H) curve, finds the Jiles-Atherton model coefficients. Supports various JA model formulations. Has an interactive GUI.

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README 

          
# jafit



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Jiles-Atherton system identification tool: Given a hysteresis loop, finds the Jiles-Atherton model coefficients.

Supports several JA model definitions.






## Usage



### Install the tool



```shell

git clone https://github.com/Zubax/jafit

cd jafit

pip install ".[interactive]"

```



You may omit `[interactive]` if you are not planning on using the GUI.



The tool works on GNU/Linux and Windows. Probably also on macOS, but YMMV.



### Solve the JA equation



The tool will plot the curves and export table files containing the data points.



```shell

# Coefficients from the default COMSOL Jiles-Atherton material model:

jafit model=venk  c_r=0.1 M_s=1.6e6 a=560 k_p=1200 alpha=0.0007



# Coefficients from the Altair Flux example B(H) curve:

jafit model=venk  H_amp_max=1111  c_r=0.2107788 M_s=1306755.22 a=108.694943 k_p=177.625645 alpha=0.000294224757



# From "Modeling of permanent magnets: Interpretation of parameters obtained from the Jiles–Atherton hysteresis model":

jafit model=orig  H_amp_min=4774648 H_amp_max=4774648  c_r=0.885 M_s=1080000 a=1107718.3824 k_p=702271.17275 alpha=3.168

```



If the H amplitude is set manually and is insufficient to reach saturation,

the resulting hysteresis loop will be a minor loop.



### Find JA coefficients for a given reference B(H) curve



The fitting problem may take multiple hours or days to solve,

depending on the curve shape and the performance of your computer.

Intermediate results and logs will be stored in the current working directory,

so it may be a good idea to create a dedicated directory for this purpose.



Basic examples:



```shell

# Fit the example curve from Altair Flux:

jafit model=venk ref="data/B(H).Altair_Flux.Example.csv" interpolate=300 H_amp_max=0



# Find coefficients for isotropic AlNiCo 5:

jafit model=venk ref="data/B(H).Jesenik.AlNiCo.tab" preg=100

```



Output symbol legend per function evaluation:

💚 -- best match so far; ❌ -- no solution (convergence failure); 🔵 -- solution exists but is not the best.

Use `quiet=1` to reduce the verbosity.



The input reference curve file must contain two columns: H \[A/m\] and B \[T\], either tab- or comma-separated.

The first row may or may not be the header row.

The reference curve may be either the entire hysteresis loop, whether major or minor,

or only a part of the descending branch.



By default, the tool will attempt to determine the suitable range of the H amplitude values using heuristics.

It is always a good idea to specify this manually instead by setting `H_amp_min` and/or `H_amp_max`,

where `H_amp_min` specifies the minimum H magnitude that must be reached before switching the H sweep direction,

and `H_amp_max` is the maximum H magnitude that the solver is allowed to use; the solver will flip the H sweep

direction somewhere between these two values as soon as the material reaches saturation ($\chi^\prime$ becomes small).



`H_amp_max` is clamped to be at least as large as `H_amp_min`;  therefore, setting `H_amp_max=0`

will effectively force the tool to use a fixed H amplitude, irrespective of the saturation detection.

If the provided loop is a minor loop, the tool needs to be instructed to limit the H amplitude to what is seen

in the reference dataset; to do that, simply pass `H_amp_max=0`.



By default, the tool will assume that the reference loop may not push the material into saturation,

and thus it will attempt to determine $M_s$ as part of the optimization problem.

By default, the range for the $M_s$ search is determined automatically using heuristics.

The heuristics can be overridden using the optional `M_s_min` and/or `M_s_max` parameters.

If `M_s_max<=M_s_min`, the tool will assume that $M_s$ is known exactly, $M_s$=`M_s_min`=`M_s_max`,

and remove the corresponding dimension from the optimization problem.

This is often useful because manufacturers often provide the saturation magnetization (or polarization) directly.

Note that for anisotropic materials, $M_s$ is usually invariant with respect to the direction of the applied field,

as it is defined by the chemical composition of the material.



Optionally, you can provide the initial guess for (some of) the coefficients: `c_r`, `M_s`, `a`, `k_p`, `alpha`.

It is required that `M_s_min <= M_s <= M_s_max`; if both min and max are provided, `M_s` is not needed.

It is usually not necessary to set `k_p` because it defaults to $H_c$, which is a reasonable guess.



The priority region error gain -- `preg` --

can be set to a value greater than one to make the optimizer assign proportionally higher importance

to the part of the descending branch where $M>0$, and the part of the ascending branch where $M<0$.

Example: `preg=100`.

This option only has effect if the full reference loop is provided.



Option `interpolate=N`, where N is a positive integer, can be used to interpolate the reference curve

with N equidistant sample points distributed along its length. Note that this is not the same as sampling the curve

on a regular H-axis grid; the difference is that the used method ensures consistent Euclidean distances between

the sample points. This is useful with irregularly sampled curves, but may cause adverse effects if the reference

curves contain large gaps, as the interpolation error within the gaps may be large.



If interpolation is not used (it is not by default), then the optimizer will naturally assign higher importance

to the regions of the curve with higher density of sample points. This may be leveraged to great advantage

if the reference curve is pre-processed to leave out the regions that are less important for the fitting.



The optimization is done in multiple stages, with global search preceding local refinement.

The tool can be instructed to skip N first stages by setting `stage=N`. See the code for details.



The tool relies on a robust implicit solver to work around stiffness that arises with certain coefficient combinations

during the optimization process. While ultra-high stiffness may be considered unphysical as it may indicate an

unrealistically high magnetic susceptibility, being able to solve such cases is useful as it improves the smoothness

of the optimization landscape, which in turn helps the optimizer converge faster and reduces the chances of

getting stuck in local minima.



For a more advanced usage example, consider the following datasheet from the manufacturer:






The following data is immediately available:



* The third quadrant of the $B(H)$ curve; the data is extracted here as `data/B(H).VegaTechnik.LNG60.tab`.



* The third quadrant polarization $J(H)$ curve is also available but not needed --- the tool will convert

  $B(H) \Rightarrow J(H) \Rightarrow M(H)$.



* Saturation magnetization $M_s=1174563$ A/m, which is expressed as saturation polarization $J_s$ in the datasheet.

  This value is usually invariant with respect to the direction of the applied field in anisotropic materials.



* The excitation field intensity $H_m=713014$ A/m.



Sadly, the datasheet is not using the SI system, so manual conversion is needed.

The corresponding optimization command is as follows:



```shell

jafit ref='data/B(H).VegaTechnik.LNG60.tab' model=venkataraman M_s_min=1174563 M_s_max=1174563 H_amp_min=713014 H_amp_max=713014

```



### Adjust the parameters interactively



Use `interactive=1` to launch an interactive tool with web GUI.

This mode requires that the interactive GUI option is enabled when installing the package;

refer to the installation section for details.



```

jafit interactive=1 model=venk ref='data/B(H).Campbell.AlNiCo_5.tab' c_r=0.00083284 M_s=1251180 a=20838 k_p=69771 alpha=0.08

```






### Helpful tips



For fetching the (approximate) data points from a third-party plot,

such as from a published paper or a material datasheet,

consider using [`trace_image.py`](https://gist.github.com/pavel-kirienko/0fcd509cd1d7c6dc2651981510badb99).



For the benefit of all mankind, please only use SI units. To convert data from a non-SI source:



- $1 \ \text{oersted} \approx 79.57747 \frac{\text{A}}{\text{m}}$

- $1 \ \frac{\text{emu}}{\text{cm}^3} = 10^3 \frac{\text{A}}{\text{m}}$

- $1 \ \text{gauss} = 10^{-4} \ \text{T}$



For more, refer to `papers/magnetic_units.pdf`.



## Validation



### Against COMSOL Multiphysics



There is a COMSOL model in the `validation` directory that contains a bored steel cylinder with a copper wire passing

along its axis.



If $B = \mu_0 (H_i + M)$ and $H_i = H - H_d$, where $H_i$ is the internal field and

$H_d = M N_d$ is the demagnetizing field with the demagnetizing factor $N_d$,

then $B = \mu_0 (H - N_d M + M)$.

Since $N_d = 0$ in the absence of free poles, the validation is performed using a loop-shaped magnet

with a magnetization wire passing through the center, which ensures $B = \mu_0 (H + M)$.



The wire carries AC magnetizing current whose amplitude is chosen to be just high enough to push the

cylinder material into saturation, while the frequency is chosen to be low to avoid eddy currents

and small-time-scale coercivity effects.

The setup is used to obtain the J(H) curve and ascertain that it matches the predictions made by the tool.






To make the prediction, run the tool specifying the JA model coefficients copied from the material properties

assigned to the cylinder in the COMSOL model,

note the shape of the curve and the predicted $H_c$, $B_r$, and $BH_\text{max}$,

and compare them against the COMSOL simulation results.



#### Specimen A: default Jiles-Atherton material



Note that we're using the same H-field amplitude as in the COMSOL model.



```shell

jafit model=venk H_amp_max=65e3 c_r=0.1 M_s=1.6e6 a=560 k_p=1200 alpha=0.0007

```






#### Specimen B: LNGT72 approximation



```shell

jafit model=venk c_r=0.00000098783341818 M_s=0931849.980906066 a=025958.529588940 k_p=147381.227474120 alpha=0.17527880356890363 H_amp_min=1e6

```






### Against Altair Flux



The following invocation results in a curve matching the example material from Altair Flux.



```shell

jafit model=venk  H_amp_max=1111  c_r=0.2107788 M_s=1306755.22 a=108.694943 k_p=177.625645 alpha=0.000294224757

```






## Development



To run verification locally, simply say `nox`.



If you want to run PyTest only, you may want to `export NUMBA_DISABLE_JIT=1` beforehand, or uninstall Numba.



To profile, go like: `python3 -m cProfile -o out.prof -m jafit ../data/bh-lng37.tab`.

Then you can use `flameprof` to visualize the collected data.



To evaluate the optimizer behaviors quickly, run the script in fast mode with `fast=1`.

This may render the results inaccurate, but it will be much faster.