Concepts

Position within the software family

  groundmeas   ──▶   groundinsight   ◀──   groundfield
  (measurement)      (reduced network            (field model,
                      model)                      PDE reference)

groundfield is the field-theoretical reference tool. It supplies the ground truth that groundinsight is measured against, and the analytic baseline against which groundmeas data can be evaluated.

Solver backends

The numerical core is swappable through a backend parameter. Eight backends share the same data model (World, Electrode, Conductor, Source) and the same Sommerfeld representation of the layered Green's function — they differ only in how the integral is evaluated:

Backend	Suitable for	Method
image	homogeneous soil	image-charge sum, closed form
image_2layer	2-layer soil	Tagg/Sunde geometric image-charge series
image_nlayer	homogeneous, 2-layer, or multi-layer (dispatcher)	image-charge dispatcher (delegates to image / image_2layer; raises for \(n\ge3\))
cim	any layered	Complex Image Method (closed form via matrix-pencil fit of \(\Gamma_1(\lambda)\))
mom	homogeneous or 2-layer	Galerkin Method-of-Moments on the closed-form layered kernels
mom_sommerfeld	any layered	Galerkin MoM with direct Sommerfeld quadrature (reference engine, slow)
bem	any layered	Boundary-element collocation with the CIM kernel
fem	any layered	Axisymmetric volume PDE with equivalent-hemisphere reduction

For homogeneous cases image is the default — evaluation reduces to a vectorised sum over image sources in NumPy. For 2-layer soils image_2layer provides a closed-form alternative based on the Tagg/Sunde image series. Engine.solve auto-dispatches "image" to image_2layer / image_nlayer based on the soil model, so notebooks written for the homogeneous case keep working when the soil is replaced by a layered one. For \(n \ge 3\) layers the closed-form path runs through cim; mom_sommerfeld is the absolute reference engine. The full engine theory is collected in Engine theory; the selection heuristic is documented in ADR-0002.

Modelling assumptions

• Quasi-static in the soil for \(f \lesssim 1\,\mathrm{kHz}\). The scalar electric potential then satisfies \(\nabla \cdot (\sigma \nabla \varphi) = 0\).
• Carson correction for the earth-return path of overhead and buried conductors; no full-wave model. See Earth-return inductive coupling below.
• Layered soil as horizontal half-spaces with piecewise constant conductivities.
• Thin-wire approximation for electrodes and conductors (Method of Moments).

Earth-return inductive coupling

Distributed conductors carry a per-segment longitudinal-impedance block whose three pieces correspond to the three physical layers of the model:

\[ Z_b(\omega) \;=\; \underbrace{R}_{\text{ohmic, ADR-0003}} \;+\;\underbrace{j\omega\,L_\text{Neumann}^{\text{(perfect mirror)}}}_{\text{inductive, ADR-0004}} \;+\;\underbrace{\Delta Z_\text{Carson}(\omega, \sigma_\text{earth}, h_i, h_j, d_{ij})}_{\text{earth-return, ADR-0005}}. \]

The first piece is purely resistive — Conductor.cross_section produces \(R = \rho_\text{mat} L / A\) per branch, available since ADR-0003. The second piece is the magnetic-image inductance under the assumption \(\sigma_\text{earth} \to \infty\) (perfect mirror); the matrix \(L_\text{Neumann}\) is built once before the frequency loop. The third piece is the Carson 1926 finite-conductivity correction (ADR-0005); it is rebuilt at every frequency because its kernel depends on \(\omega\) through the dimensionless Carson parameter \(a = D\sqrt{\omega\mu_0\sigma_\text{earth}}\), with \(D = 2h_i\) for self and \(D = \sqrt{(h_i+h_j)^2 + d_{ij}^2}\) for mutual.

Two engine-side switches govern this block:

• Conductor.inductance_model = "neumann" activates the second piece; without it the system is purely real and the historic DC fast path is preserved bit-exact.
• Engine.earth_inductive_model selects the third-piece model:
  • "perfect_mirror" (default, ADR-0004) — no third piece, the earth is a perfect magnetic mirror.
  • "carson_series" (ADR-0005) — Carson 1926 per-meter formula scaled by segment length. Asymptotically correct for long parallel wires over homogeneous earth; an approximation for short wires or layered soils.
  • "sommerfeld" (ADR-0006) — geometric integration of the σ-dependent vector-potential Green's function over the actual segment-pair geometry, with native support for layered earth (Pollaczek/Wait kernel). Rigorous for any wire length and orientation; converges to "carson_series" on the cluster-impedance level for long parallel wires over homogeneous earth.

The natural diagnostic for this block is the soil skin depth \(\delta(\omega) = \sqrt{2/(\omega\mu_0\sigma_\text{earth})} \approx 503\sqrt{\rho_\text{earth}/f}\,\mathrm{m}\), exposed at every solved frequency through FieldResult.metadata["penetration_depth"]. For the quasi-static frequencies (≤ 1 kHz) and resistivities (50–5000 Ω·m) the skin depth ranges from ≈ 350 m to ≈ 35 km — comparable to or larger than typical TN low-voltage distances, which is exactly why Carson matters.

The rho-f model

The objective of a field computation in groundfield is not only a numerical result but also a compression of the solution down to a small set of parametrically readable quantities. For the two-port case that compression is the rho-f model:

\[ Z(\rho, f) = R_{0}(\rho) + X(\rho, f) \]

where \(R_{0}\) is the low-frequency spreading resistance and \(X\) collects the frequency-dependent reactive contributions. The coefficients are fitted against the field solution and end up as BusType.impedance_formula in groundinsight.

Typical questions

groundfield is built around investigations of TN distribution networks with substation, house connections, and cable cabinets in layered soil. The core questions are:

1. How strongly does a remote current injection influence the grounding-measurement result?
2. How important are coupling and return-path effects in the low-frequency range?
3. Can robust statements be derived for typical distribution-network configurations?

groundfield provides the numerical basis for answering these questions.