Parallel Transforms#

In most BOUT++ simulations the Y coordinate is parallel to the magnetic field. In particular if the magnetic field \(\mathbf{B}\) can be expressed as

\[\mathbf{B} = \nabla z \times \nabla x\]

then the Clebsch operators can be used. See section Differential operators for more details.

The structure of the magnetic field can be simple, as in a slab geometry, but in many cases it is quite complicated. In a tokamak, for example, the magnetic shear causes deformation of grid cells and numerical issues. One way to overcome this is to transform between local coordinate systems, interpolating in the toroidal (Z) direction when calculating gradients along the magnetic field. This is called the shifted metric method. In more general geometries such as stellarators, the magnetic field can have a 3D structure and stochastic regions. In this case the interpolation becomes 2D (in X and Z), and is known as the Flux Coordinate Independent (FCI) method.

To handle these different cases in the same code, the BOUT++ mesh implements different ParallelTransform classes. Each Field3D class contains a pointer to the values up and down in the Y direction, called yup and ydown. These values are calculated during communication (unless explicitly disabled, see Aligned transform):

Field3D f(0.0);  // f allocated, set to zero
f.yup();    // error: f.yup not allocated

f.yup(); // ok

f.ydown()(0,1,0); // ok

In the case of slab geometry, yup and ydown point to the original field (f). For this reason the value of f along the magnetic field from f(x,y,z) is given by f.ydown(x,y-1,z) and f.yup(x,y+1,z). To take a centred difference along Y using the Field3D iterators (section Iterating over fields):

Field3D result;
result.allocate(); // Need to allocate before indexing

for(const auto &i : result.region(RGN_NOBNDRY)) {
  result[i] = f.yup()[i.yp()] - f.ydown()[i.ym()];

Note the use of yp() and ym() to increase and decrease the Y index.

Parallel derivatives or interpolations can also be calculated by transforming to a globally field aligned grid, Aligned transform. This method is also used as a fallback when the input does not have parallel slices calculated when using Shifted metric.

Field-aligned grid#

The default ParallelTransform is the identity transform, which sets yup() and ydown() to point to the same field. In the input options the setting is

type = identity

This then uses the ParallelTransformIdentity class to calculate the yup and ydown fields.

This is mostly useful for slab geometries, where for a straight magnetic field the grid is either periodic in the y-direction or ends on a y-boundary. By setting the global option TwistShift = true and providing a ShiftAngle in the gridfile or [mesh] options a branch cut can be introduced between the beginning and end of the y-domain.

ParallelTransformIdentity can also be used in non-slab geometries. Then TwistShift = true should be set so that a twist-shift boundary condition is applied on closed field lines, as field-line following coordinates are not periodic in poloidal angle. Note that it is not recommended to use ParallelTransformIdentity with toroidal geometries, as magnetic shear will make the radial derivatives inaccurate away from the outboard midplane (which is normall chosen as the zero point for the integrated shear).

Shifted metric#

The shifted metric method is selected using:

type = shifted

so that mesh uses the ShiftedMetric class to calculate parallel transforms. During initialisation, this class reads a quantity zShift from the input or grid file. If zShift is not found then qinty is read instead. If qinty is not found then the angle is zero, and this method becomes the same as the identity transform. For each X and Z index, the zShift variable should contain the toroidal angle of a magnetic field line at \(z=0\) starting at \(\phi=0\) at a reference location \(\theta_0\):

\[\mathtt{zShift} = \int_{\theta_0}^\theta \frac{B_\phi h_\theta}{B_\theta R} d\theta\]

Note that here \(\theta_0\) does not need to be constant in X (radius), since it is only the relative shifts between Y locations which matters.

Special handling is needed for parallel boundary conditions, see Shifted metric boundary conditions.

Aligned transform#

The aligned transform method is a variation of shifted metric. Parallel derivatives are calculated by transforming their argument to a globally field aligned mesh, by toroidal interpolation using zShift, calculating the derivative or interpolation on the globally aligned grid, and then transforming the result back to the standard toroidal grid.

The aligned transform scheme is implemented using the ShiftedMetric class for parallel transforms, by disabling the calculation of parallel slices. Select it by using:

type = shifted
calcParallelSlices_on_communicate = false

With these settings, inputs to parallel derivative or interpolation operators will be implicitly transformed to the globally aligned grid, and the results transformed back.

Using implicit transformations can result in more interpolations than absolutely necessary being done. For example, when using y-staggered grids, most variables will need both a parallel interpolation between CELL_CENTRE and CELL_YLOW and also at least one parallel derivative. To optimise such cases, the field aligned version of a variable can be calculated and stored in a separate object. BOUT++ operators return their result on the same grid as the input argument, so if the result of an operation on a field aligned variable is needed on the toroidal grid, it must be transformed explicitly. For example, parallel diffusion of a variable f in this scheme might look something like:

f_aligned = toFieldAligned(f);

ddt(f) = D_par * fromFieldAligned(Grad2_par2(f_aligned));

Special handling is needed for parallel boundary conditions, see Aligned transform boundary conditions.

FCI method#

To use the FCI method for parallel transforms, set

type = fci

which causes the FCITransform class to be used for parallel transforms. This reads four variables (3D fields) from the input grid: forward_xt_prime, forward_zt_prime, backward_xt_prime, and backward_zt_prime. These give the cell indices, not in general integers, in the forward (yup) and backward (ydown) directions. These are arranged so that forward_xt_prime(x,y,z) is the x index at y+1. Hence f.yup()(x,y+1,z) is calculated using forward_xt_prime(x,y,z) and forward_zt_prime(x,y,z), whilst f.ydown()(x,y-1,z) is calculated using backward_xt_prime(x,y,z) and backward_zt_prime(x,y,z).

Tools for calculating these mappings include Zoidberg, a Python tool which carries out field-line tracing and generates FCI inputs.

Special handling is needed for parallel boundary conditions, see FCI boundary conditions.