More efficient PML BoxArray (#2631)

* More efficient PML BoxArray

If the union of the grids is a single rectangular domain, we can simplify
the process and generate more efficient PML BoxArray.

* Update Source/BoundaryConditions/PML.cpp

Co-authored-by: Edoardo Zoni <59625522+EZoni@users.noreply.github.com>

* Apply suggestions from code review

* reset Python_wrappers benchmark

* fix the computation of sigmas for the new BoxArray

* Revert "reset Python_wrappers benchmark"

This reverts commit 2999304571d525076ba6c1f7cbbcef6e9acafabb.

* fix warning

* fix 1d

* initialize to quiet NaN

* Reset Benchmark: pml_x_psatd

- maximum relative error: 2.50e-06
- new implementation: 10 PML grids
- old implementation: 24 PML grids

* Reset Benchmark: LaserAccelerationMR

- maximum relative error: 2.73e-04
- new implementation: (18,8,8) PML grids
- old implementation: (48,18,18) PML grids

* Reset Benchmark: LaserOnFine

- maximum relative error: 6.44e-05
- new implementation: (2,6,6) PML grids
- old implementation: (2,12,12) PML grids

* Reset Benchmark: PlasmaAccelerationMR

- maximum relative error: 6.84e-04
- new implementation: (10,6,6) PML grids
- old implementation: (24,12,12) PML grids

* Reset Benchmark: RefinedInjection

- maximum relative error: 2.55e-04
- new implementation: (18,8,8) PML grids
- old implementation: (48,18,18) PML grids

* Reset Benchmark: momentum-conserving-gather

- maximum relative error: 7.43e-04
- new implementation: (10,6,6) PML grids
- old implementation: (24,12,12) PML grids

* Reset Benchmark: subcyclingMR

- maximum relative error: 2.41e-05
- new implementation: (6,6,6) PML grids
- old implementation: (12,12,12) PML grids

* Reset Benchmark: Langmuir_multi_2d_MR

- maximum relative error: 1.32e-01 (B numerical artifact)
- new implementation: (0,20,20) PML grids
- old implementation: (0,52,40) PML grids

* Reset Benchmark: Langmuir_multi_2d_MR_psatd

- maximum relative error: 1.05e-01 (B numerical artifact)
- new implementation: (0,20,20) PML grids
- old implementation: (0,52,40) PML grids

* Reset Benchmark: Python_LaserAccelerationMR

- maximum relative error: 2.73e-04
- new implementation: (18,8,8) PML grids
- old implementation: (48,18,18) PML grids

* Reset Benchmark: Python_wrappers

- maximum relative error: 1.07e-08
- new implementation: 8 PML grids
- old implementation: 16 PML grids

* Reset Benchmark: pml_psatd_dive_divb_cleaning

- maximum relative error: 4.91e-03
- new implementation: 24 PML grids
- old implementation: 98 PML grids

* Remove an assertion.  We will fix it later

* Reset Benchmark: Langmuir_multi_2d_MR_anisotropic

- maximum relative error: 1.07e-01 (B numerical artifact)
- new implementation: (0,16,16) PML grids
- old implementation: (0,40,34) PML grids

* Reset Benchmark: PEC_field_mr

- maximum relative error: 3.98e-02
- new implementation: (0,2,2) PML grids
- old implementation: (0,2,2) PML grids
  (different number of ghost cells on coarse PML patch)

Co-authored-by: Edoardo Zoni <59625522+EZoni@users.noreply.github.com>
Co-authored-by: Edoardo Zoni <ezoni@lbl.gov>

1 files changed, 8 insertions, 8 deletions

diff --git a/Examples/Tests/PythonWrappers/PICMI_inputs_2d.py b/Examples/Tests/PythonWrappers/PICMI_inputs_2d.py
index c34c9cf80..2f9b16952 100755
--- a/Examples/Tests/PythonWrappers/PICMI_inputs_2d.py
+++ b/Examples/Tests/PythonWrappers/PICMI_inputs_2d.py
@@ -256,7 +256,7 @@ atol = 1e-12
 
 # E
 check_values(1013263608.6369569, Ex[:,:], rtol, atol)
-check_values(717278253.4505507 , Ey[:,:], rtol, atol)
+check_values(717278256.7957529 , Ey[:,:], rtol, atol)
 check_values(717866566.5718911 , Ez[:,:], rtol, atol)
 # B
 check_values(3.0214509313437636, Bx[:,:], rtol, atol)
@@ -267,22 +267,22 @@ check_values(3.0188584528062377, F[:,:], rtol, atol)
 check_values(1013672631.8764204, G[:,:], rtol, atol)
 # E in PML
 check_values(364287936.1526477 , Expml[:,:,0], rtol, atol)
-check_values(183582351.3212558 , Expml[:,:,1], rtol, atol)
+check_values(183582352.20753333, Expml[:,:,1], rtol, atol)
 check_values(190065766.41491824, Expml[:,:,2], rtol, atol)
-check_values(440581905.9336025 , Eypml[:,:,0], rtol, atol)
-check_values(178117293.6629357 , Eypml[:,:,1], rtol, atol)
+check_values(440581907.0828975 , Eypml[:,:,0], rtol, atol)
+check_values(178117294.05871135, Eypml[:,:,1], rtol, atol)
 check_values(0.0               , Eypml[:,:,2], rtol, atol)
 check_values(430277101.26568377, Ezpml[:,:,0], rtol, atol)
 check_values(0.0               , Ezpml[:,:,1], rtol, atol)
 check_values(190919663.2167449 , Ezpml[:,:,2], rtol, atol)
 # B in PML
 check_values(1.0565189315366146 , Bxpml[:,:,0], rtol, atol)
-check_values(0.4618191395098556 , Bxpml[:,:,1], rtol, atol)
-check_values(0.6849858273929585 , Bxpml[:,:,2], rtol, atol)
+check_values(0.46181913800643065, Bxpml[:,:,1], rtol, atol)
+check_values(0.6849858305343736 , Bxpml[:,:,2], rtol, atol)
 check_values(1.7228584190213505 , Bypml[:,:,0], rtol, atol)
-check_values(0.47697331996765685, Bypml[:,:,1], rtol, atol)
+check_values(0.47697332248020935, Bypml[:,:,1], rtol, atol)
 check_values(0.0                , Bypml[:,:,2], rtol, atol)
-check_values(1.5183380774611628 , Bzpml[:,:,0], rtol, atol)
+check_values(1.518338068658267  , Bzpml[:,:,0], rtol, atol)
 check_values(0.0                , Bzpml[:,:,1], rtol, atol)
 check_values(0.6849858291863835 , Bzpml[:,:,2], rtol, atol)
 # F and G in PML