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The Open FUSION Toolkit 1.0.0-6f445ef
An open-source framework for fusion and plasma science and engineering
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The Open FUSION Toolkit 1.0.0-6f445ef
An open-source framework for fusion and plasma science and engineering
|
In this example we demonstrate how to compute frequency response for a model from both coils and the plasma mode computed in ThinCurr Python Example: Compute current potential from B-norm.
pip or other standard methods.To load the ThinCurr python module we need to tell python where to the module is located. This can be done either through the PYTHONPATH environment variable or within a script using sys.path.append() as below, where we look for the environement variable OFT_ROOTPATH to provide the path to where the OpenFUSIONToolkit is installed (/Applications/OFT for binaries on macOS).
We now create a OFT_env instance for execution using four threads and a ThinCurr instance that utilizes that execution environment. Once created, we setup the model from an existing HDF5 and XML mesh definition using setup_model().
We also initialize I/O for this model using setup_io() to enable output of plotting files for 3D visualization in VisIt, Paraview, or using pyvista below.
#----------------------------------------------
Open FUSION Toolkit Initialized
Development branch: tMaker_eq_split
Revision id: 6b5d2f29
Parallelization Info:
# of MPI tasks = 1
# of NUMA nodes = 1
# of OpenMP threads = 4
Integer Precisions = 4 8
Float Precisions = 4 8 16
Complex Precisions = 4 8
LA backend = native
#----------------------------------------------
Creating thin-wall model
No V(t) driver coils found
Loading I(t) driver coils
Masked 0 coils from sensors
Building holes
Loading region resistivity:
1 1.2570E-05
Setup complete:
# of points = 2394
# of edges = 7182
# of cells = 4788
# of holes = 2
# of closures = 0
# of Vcoils = 0
# of Icoils = 1
Before running the main calculations we will also define some sensors to measure the magnetic field. In ThinCurr all sensors measure the flux passing through a 3D path of points, but there are several helper classes to define common sensors (eg. Poloidal flux and Mirnovs). Here we define two Mirnov sensors to measure the Z-component of the magnetic field 5 cm on either side of the torus. save_sensors() is then used to save the resulting sensor for later use.
With the model setup, we can now compute the self-inductance and resistivity matrices. A numpy version of the self-inductance matrix will be stored at tw_plate.Lmat. By default the resistivity matrix is not moved to python as it is sparse and converting to dense representation would require an increase in memory. These matrices correspond to the \(\textrm{L}\) and \(\textrm{R}\) matrices for the physical system
\(\textrm{L} \frac{\partial I}{\partial t} + \textrm{R} I = V\)
Building element<->element self inductance matrix Time = 3s Building resistivity matrix
For the first case we will compute the frequency response on the model to current driven in the coil set specified in oft_in.xml at 1 kHz. To do this we first compute the coil to model mutual inductance matrix using tw_plate.compute_Mcoil(). Then we compute a purely real driver voltage by using the first row of this matrix (equivalent to multiplying by 1). Finally we use tw_plate.compute_freq_response() to compute the frequency response to this input.
Building coil<->element inductance matrices
Time = 0s
Starting frequency-domain run
Frequency [Hz] = 1.00000E+03
Starting complex GMRES solver
0 0.000000E+00 1.133720E+01
1 6.361443E+00
2 3.043293E+00
3 2.548610E+00
4 1.414304E+00
5 7.091233E-01
6 3.568591E-01
7 1.401280E-01
8 8.187974E-02
9 6.821360E-02
10 6.535723E-02
11 6.196449E-02
12 4.684791E-02
13 4.554808E-02
14 3.249582E-02
15 3.176619E-02
16 2.835578E-02
17 2.702281E-02
18 2.556674E-02
19 2.428017E-02
20 2.316560E-02
21 2.232681E-02
22 2.148619E-02
23 2.097776E-02
24 2.059959E-02
25 2.033502E-02
26 1.999214E-02
27 1.970446E-02
28 1.954535E-02
29 1.942117E-02
30 1.930082E-02
31 1.915314E-02
32 1.899206E-02
33 1.889307E-02
34 1.879785E-02
35 1.871871E-02
36 1.860217E-02
37 1.837916E-02
38 1.818528E-02
39 1.797928E-02
40 1.781565E-02
41 1.766273E-02
42 1.751544E-02
43 1.727815E-02
44 1.694255E-02
45 1.652836E-02
46 1.561365E-02
47 1.444366E-02
48 1.322331E-02
49 1.144057E-02
50 9.537696E-03
51 7.858352E-03
52 6.457814E-03
53 5.338199E-03
54 4.295388E-03
55 3.182877E-03
56 2.733069E-03
57 2.341214E-03
58 1.861867E-03
59 1.435584E-03
60 2.097016E-01 1.077125E-03 5.136466E-03
61 1.063746E-03
62 9.427853E-04
63 8.505541E-04
64 8.353258E-04
65 8.047516E-04
66 7.724182E-04
67 7.150750E-04
68 6.694745E-04
69 5.729630E-04
70 4.387715E-04
71 4.212803E-04
72 3.855457E-04
73 3.713219E-04
74 3.494009E-04
75 3.400814E-04
76 3.086956E-04
77 2.973514E-04
78 2.749248E-04
79 2.687598E-04
80 2.607900E-04
81 2.575348E-04
82 2.533340E-04
83 2.501205E-04
84 2.479021E-04
85 2.455230E-04
86 2.427133E-04
87 2.405284E-04
88 2.390289E-04
89 2.375186E-04
90 2.359244E-04
91 2.343140E-04
92 2.334188E-04
93 2.325685E-04
94 2.314972E-04
95 2.302529E-04
96 2.291000E-04
97 2.271692E-04
98 2.233233E-04
99 2.184365E-04
100 2.130894E-04
101 2.073864E-04
102 2.030197E-04
103 1.966233E-04
104 1.876042E-04
105 1.794986E-04
106 1.719932E-04
107 1.559747E-04
108 1.285299E-04
109 1.049224E-04
110 9.157738E-05
111 8.206501E-05
112 7.053876E-05
113 5.625970E-05
114 4.082929E-05
115 2.925797E-05
116 2.446758E-05
117 2.099124E-05
118 1.916679E-05
119 1.812661E-05
120 2.105014E-01 1.787020E-05 8.489348E-05
121 1.770476E-05
122 1.730159E-05
123 1.729363E-05
124 1.728695E-05
125 1.713555E-05
126 1.709848E-05
127 1.705771E-05
128 1.696257E-05
129 1.696124E-05
130 1.690325E-05
131 1.682883E-05
132 1.680193E-05
133 1.642649E-05
134 1.626680E-05
135 1.600319E-05
136 1.593859E-05
137 1.565963E-05
138 1.543334E-05
139 1.497474E-05
140 1.408529E-05
141 1.306096E-05
142 1.194754E-05
143 1.089594E-05
144 9.957490E-06
145 9.174953E-06
146 8.317284E-06
147 7.236062E-06
148 6.233711E-06
149 5.392590E-06
150 4.718667E-06
151 4.055740E-06
152 3.685105E-06
153 3.376327E-06
154 3.263667E-06
155 3.102994E-06
156 2.888878E-06
157 2.591837E-06
158 2.212184E-06
159 1.897751E-06
160 1.626459E-06
161 1.379171E-06
162 1.165506E-06
163 1.036990E-06
164 9.623257E-07
165 9.173395E-07
166 8.906975E-07
167 8.739453E-07
168 8.622998E-07
169 8.550025E-07
170 8.470847E-07
171 8.421507E-07
172 8.345323E-07
173 8.262945E-07
174 8.131854E-07
175 8.045242E-07
176 7.964064E-07
177 7.861783E-07
178 7.800316E-07
179 7.730920E-07
180 2.104196E-01 7.443755E-07 3.537576E-06
181 7.429977E-07
182 7.344187E-07
183 7.231447E-07
184 7.198199E-07
185 7.146831E-07
186 6.936397E-07
187 6.831498E-07
188 6.754092E-07
189 6.672311E-07
190 6.621124E-07
191 6.565343E-07
192 6.480084E-07
193 6.414702E-07
194 6.367620E-07
195 6.298508E-07
196 6.264528E-07
197 6.233235E-07
198 6.201443E-07
199 6.172521E-07
200 6.126088E-07
201 6.085881E-07
202 6.049502E-07
203 5.994509E-07
204 5.936265E-07
205 5.819052E-07
206 5.677349E-07
207 5.470759E-07
208 5.188072E-07
209 4.763159E-07
210 4.111860E-07
211 3.470847E-07
212 2.907945E-07
213 2.629491E-07
214 2.401745E-07
215 2.232993E-07
216 1.843202E-07
217 1.595641E-07
218 1.381624E-07
219 1.161613E-07
220 7.696194E-08
221 5.301292E-08
222 3.165613E-08
223 1.976375E-08
224 1.404508E-08
225 1.089556E-08
226 8.432231E-09
227 6.929075E-09
228 5.766957E-09
229 4.958958E-09
230 4.456666E-09
231 4.054642E-09
232 3.678141E-09
233 3.392003E-09
234 3.137053E-09
235 2.851272E-09
236 2.507529E-09
237 2.115919E-09
238 1.782318E-09
239 1.639938E-09
240 2.104221E-01 1.549768E-09 7.365041E-09
Time = 7.80307E-01
The resulting currents are saved for plotting using tw_plate.save_current(). Here we save the real (Jr) and Imaginary (Ji) components of the response for visualization. Once all fields have been saved for plotting tw_plate.build_XDMF() to generate the XDMF descriptor files for plotting with VisIt of Paraview.
Creating output files: oft_xdmf.XXXX.h5
Removing old Xdmf files
Removed 24 files
Found Group: thincurr
Found Mesh: icoils
# of blocks: 1
Found Mesh: smesh
# of blocks: 1
We can also compute the pickup of sensors in response to both the coil and the eddy currents. To do this we compute the mutual coupling matrices between the sensors and model and the sensors and the driver coils (icoils).
Loading sensor information
Loading flux loops from file: floops.loc
# of floops =
␂
Building element->sensor inductance matrix
Time = 0s
Building coil->sensor inductance matrix
Time = 0s
Real: -4.91283E-06, Imaginary: -1.37366E-08 Real: 3.89380E-06, Imaginary: -1.95325E-08
We now create a second ThinCurr instance for the plasma mode utilizes the same execution environment as above. For this case we also load the plasma mode current patterns created in ThinCurr Python Example: Compute current potential from B-norm.
Creating thin-wall model
No V(t) driver coils found
No I(t) driver coils found
Building holes
Setup complete:
# of points = 6320
# of edges = 18960
# of cells = 12640
# of holes = 3
# of closures = 2
# of Vcoils = 0
# of Icoils = 0
To use this current distribution we need the inductive coupling between the mode currents and the ThinCurr model of the wall (tw_torus). This can be done using cross_eval(), which in this case computes the flux through each element on tw_torus due to the currents specified by weights mode_drive corresponding to the model tw_mode.
Applying MF element<->element inductance matrix Time = 17s
We node compute the frequency-response as above using our new driver flux.
Starting frequency-domain run
Frequency [Hz] = 1.00000E-03
Starting complex GMRES solver
0 0.000000E+00 5.096814E-04
1 3.658730E-04
2 1.703892E-04
3 1.084359E-04
4 8.205521E-05
5 6.865622E-05
6 5.993770E-05
7 4.914546E-05
8 4.184366E-05
9 3.445982E-05
10 2.726525E-05
11 2.155864E-05
12 1.642650E-05
13 1.254608E-05
14 1.065632E-05
15 9.146161E-06
16 8.042828E-06
17 7.326010E-06
18 6.754809E-06
19 6.139702E-06
20 5.609269E-06
21 5.363225E-06
22 4.659498E-06
23 4.239346E-06
24 3.958802E-06
25 3.641633E-06
26 3.392575E-06
27 3.245019E-06
28 3.032400E-06
29 2.881514E-06
30 2.772396E-06
31 2.638970E-06
32 2.512160E-06
33 2.398754E-06
34 2.223389E-06
35 2.084855E-06
36 2.000267E-06
37 1.902716E-06
38 1.807629E-06
39 1.749937E-06
40 1.692552E-06
41 1.638355E-06
42 1.600036E-06
43 1.556587E-06
44 1.511407E-06
45 1.472888E-06
46 1.428734E-06
47 1.385139E-06
48 1.353518E-06
49 1.322420E-06
50 1.288573E-06
51 1.255706E-06
52 1.220621E-06
53 1.183304E-06
54 1.148526E-06
55 1.117186E-06
56 1.086858E-06
57 1.058915E-06
58 1.031573E-06
59 1.007341E-06
60 1.688020E-04 9.784296E-07 5.796316E-03
61 9.774667E-07
62 9.433920E-07
63 9.339904E-07
64 9.278808E-07
65 9.087590E-07
66 8.968423E-07
67 8.870132E-07
68 8.732203E-07
69 8.584597E-07
70 8.477024E-07
71 8.341029E-07
72 8.224047E-07
73 8.123651E-07
74 8.031058E-07
75 7.919815E-07
76 7.823907E-07
77 7.738244E-07
78 7.636894E-07
79 7.542063E-07
80 7.451934E-07
81 7.343870E-07
82 7.246090E-07
83 7.143563E-07
84 7.024065E-07
85 6.901207E-07
86 6.790210E-07
87 6.663489E-07
88 6.532869E-07
89 6.446003E-07
90 6.356244E-07
91 6.253252E-07
92 6.168966E-07
93 6.072568E-07
94 5.965689E-07
95 5.870435E-07
96 5.773067E-07
97 5.670977E-07
98 5.582529E-07
99 5.491235E-07
100 5.399116E-07
101 5.340371E-07
102 5.286540E-07
103 5.223775E-07
104 5.161372E-07
105 5.107365E-07
106 5.048325E-07
107 4.999185E-07
108 4.952414E-07
109 4.906883E-07
110 4.878797E-07
111 4.856959E-07
112 4.841160E-07
113 4.827687E-07
114 4.812884E-07
115 4.796780E-07
116 4.774917E-07
117 4.755219E-07
118 4.735538E-07
119 4.715701E-07
120 1.732213E-04 4.697510E-07 2.711855E-03
121 4.697401E-07
122 4.669075E-07
123 4.662151E-07
124 4.654691E-07
125 4.640588E-07
126 4.629983E-07
127 4.622488E-07
128 4.615967E-07
129 4.607027E-07
130 4.602552E-07
131 4.596594E-07
132 4.585647E-07
133 4.574531E-07
134 4.560433E-07
135 4.543052E-07
136 4.523347E-07
137 4.502423E-07
138 4.480490E-07
139 4.457407E-07
140 4.437707E-07
141 4.420414E-07
142 4.403816E-07
143 4.385391E-07
144 4.362389E-07
145 4.342612E-07
146 4.323150E-07
147 4.296061E-07
148 4.274409E-07
149 4.253763E-07
150 4.229276E-07
151 4.210598E-07
152 4.193290E-07
153 4.172124E-07
154 4.148740E-07
155 4.125468E-07
156 4.100566E-07
157 4.077573E-07
158 4.060159E-07
159 4.043013E-07
160 4.023694E-07
161 4.005185E-07
162 3.982753E-07
163 3.956719E-07
164 3.937746E-07
165 3.918465E-07
166 3.895589E-07
167 3.874751E-07
168 3.853233E-07
169 3.827543E-07
170 3.804144E-07
171 3.780222E-07
172 3.751513E-07
173 3.720707E-07
174 3.694219E-07
175 3.668359E-07
176 3.642981E-07
177 3.613587E-07
178 3.591645E-07
179 3.565894E-07
180 1.852839E-04 3.538349E-07 1.909690E-03
181 3.537856E-07
182 3.500512E-07
183 3.482831E-07
184 3.478026E-07
185 3.459529E-07
186 3.431617E-07
187 3.418233E-07
188 3.406959E-07
189 3.382621E-07
190 3.366068E-07
191 3.354724E-07
192 3.341931E-07
193 3.328302E-07
194 3.317556E-07
195 3.305822E-07
196 3.294248E-07
197 3.284378E-07
198 3.275170E-07
199 3.264763E-07
200 3.253748E-07
201 3.240501E-07
202 3.226209E-07
203 3.213771E-07
204 3.200755E-07
205 3.185309E-07
206 3.170506E-07
207 3.153662E-07
208 3.136717E-07
209 3.123916E-07
210 3.108325E-07
211 3.091989E-07
212 3.077751E-07
213 3.059368E-07
214 3.042218E-07
215 3.028813E-07
216 3.012607E-07
217 2.995378E-07
218 2.979456E-07
219 2.959912E-07
220 2.941971E-07
221 2.928459E-07
222 2.913869E-07
223 2.895411E-07
224 2.878474E-07
225 2.864619E-07
226 2.850007E-07
227 2.837177E-07
228 2.826282E-07
229 2.815227E-07
230 2.806556E-07
231 2.799656E-07
232 2.793760E-07
233 2.788317E-07
234 2.782611E-07
235 2.775939E-07
236 2.766984E-07
237 2.758602E-07
238 2.749035E-07
239 2.738438E-07
240 1.927548E-04 2.728285E-07 1.415417E-03
241 2.728178E-07
242 2.710476E-07
243 2.705987E-07
244 2.704858E-07
245 2.696865E-07
246 2.689027E-07
247 2.684794E-07
248 2.680546E-07
249 2.673946E-07
250 2.669733E-07
251 2.666009E-07
252 2.660659E-07
253 2.654272E-07
254 2.647873E-07
255 2.640171E-07
256 2.629210E-07
257 2.618660E-07
258 2.607966E-07
259 2.595534E-07
260 2.585882E-07
261 2.578020E-07
262 2.568480E-07
263 2.556991E-07
264 2.545187E-07
265 2.532987E-07
266 2.521151E-07
267 2.508233E-07
268 2.494598E-07
269 2.481572E-07
270 2.467712E-07
271 2.455192E-07
272 2.442293E-07
273 2.428922E-07
274 2.415521E-07
275 2.402424E-07
276 2.386617E-07
277 2.373005E-07
278 2.363280E-07
279 2.353700E-07
280 2.342949E-07
281 2.332229E-07
282 2.320650E-07
283 2.307464E-07
284 2.297830E-07
285 2.288087E-07
286 2.276978E-07
287 2.264686E-07
288 2.253118E-07
289 2.240665E-07
290 2.228522E-07
291 2.218667E-07
292 2.207404E-07
293 2.191579E-07
294 2.178242E-07
295 2.165109E-07
296 2.150646E-07
297 2.135990E-07
298 2.126607E-07
299 2.115731E-07
300 2.043648E-04 2.103251E-07 1.029165E-03
301 2.102987E-07
302 2.082152E-07
303 2.075436E-07
304 2.066778E-07
305 2.058953E-07
306 2.044761E-07
307 2.036532E-07
308 2.027026E-07
309 2.013282E-07
310 2.006290E-07
311 2.000476E-07
312 1.991555E-07
313 1.983852E-07
314 1.977321E-07
315 1.968598E-07
316 1.960436E-07
317 1.954054E-07
318 1.947795E-07
319 1.941130E-07
320 1.933968E-07
321 1.924687E-07
322 1.916268E-07
323 1.909389E-07
324 1.901888E-07
325 1.892960E-07
326 1.883355E-07
327 1.872679E-07
328 1.862989E-07
329 1.854958E-07
330 1.843815E-07
331 1.833439E-07
332 1.826677E-07
333 1.817328E-07
334 1.806330E-07
335 1.798724E-07
336 1.790535E-07
337 1.781661E-07
338 1.773827E-07
339 1.763765E-07
340 1.755160E-07
341 1.749810E-07
342 1.744300E-07
343 1.736521E-07
344 1.729156E-07
345 1.723124E-07
346 1.716604E-07
347 1.711524E-07
348 1.707035E-07
349 1.702057E-07
350 1.698123E-07
351 1.694570E-07
352 1.691490E-07
353 1.688680E-07
354 1.684849E-07
355 1.680500E-07
356 1.676557E-07
357 1.672767E-07
358 1.667614E-07
359 1.660397E-07
360 2.102327E-04 1.653286E-07 7.864074E-04
361 1.653180E-07
362 1.640641E-07
363 1.637815E-07
364 1.637210E-07
365 1.632708E-07
366 1.628530E-07
367 1.626228E-07
368 1.623508E-07
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1104 2.219281E-09
1105 2.212429E-09
1106 2.205261E-09
1107 2.196193E-09
1108 2.186408E-09
1109 2.172877E-09
1110 2.150740E-09
1111 2.132691E-09
1112 2.118049E-09
1113 2.097038E-09
1114 2.065766E-09
1115 2.029390E-09
1116 1.990280E-09
1117 1.948526E-09
1118 1.912768E-09
1119 1.867413E-09
1120 1.815623E-09
1121 1.776532E-09
1122 1.731617E-09
1123 1.666774E-09
1124 1.613393E-09
1125 1.570189E-09
1126 1.521959E-09
1127 1.479038E-09
1128 1.445707E-09
1129 1.411985E-09
1130 1.367017E-09
1131 1.311827E-09
1132 1.261920E-09
1133 1.223118E-09
1134 1.197696E-09
1135 1.177030E-09
1136 1.161479E-09
1137 1.149790E-09
1138 1.142785E-09
1139 1.134040E-09
1140 2.439946E-04 1.128253E-09 4.624091E-06
1141 1.128248E-09
1142 1.118277E-09
1143 1.115308E-09
1144 1.112859E-09
1145 1.102898E-09
1146 1.089687E-09
1147 1.071673E-09
1148 1.058069E-09
1149 1.030503E-09
1150 1.004796E-09
1151 9.773446E-10
1152 9.406890E-10
1153 9.232930E-10
1154 9.035115E-10
1155 8.841477E-10
1156 8.653353E-10
1157 8.457153E-10
1158 8.235072E-10
1159 7.948835E-10
1160 7.652732E-10
1161 7.386581E-10
1162 7.175083E-10
1163 6.994915E-10
1164 6.852354E-10
1165 6.719086E-10
1166 6.588096E-10
1167 6.443444E-10
1168 6.318688E-10
1169 6.249724E-10
1170 6.199058E-10
1171 6.152037E-10
1172 6.095455E-10
1173 6.053938E-10
1174 6.018876E-10
1175 5.994173E-10
1176 5.975521E-10
1177 5.957404E-10
1178 5.937344E-10
1179 5.917070E-10
1180 5.895290E-10
1181 5.881436E-10
1182 5.869672E-10
1183 5.855873E-10
1184 5.842856E-10
1185 5.833754E-10
1186 5.823962E-10
1187 5.813061E-10
1188 5.800243E-10
1189 5.790345E-10
1190 5.782520E-10
1191 5.776077E-10
1192 5.770111E-10
1193 5.762849E-10
1194 5.756187E-10
1195 5.746926E-10
1196 5.736278E-10
1197 5.725298E-10
1198 5.711262E-10
1199 5.694011E-10
1200 2.440139E-04 5.674469E-10 2.325470E-06
1201 5.673479E-10
1202 5.639526E-10
1203 5.628152E-10
1204 5.619747E-10
1205 5.609928E-10
1206 5.598708E-10
1207 5.594168E-10
1208 5.589611E-10
1209 5.580011E-10
1210 5.577036E-10
1211 5.573621E-10
1212 5.564515E-10
1213 5.557109E-10
1214 5.547904E-10
1215 5.537243E-10
1216 5.527440E-10
1217 5.517615E-10
1218 5.505458E-10
1219 5.492629E-10
1220 5.481813E-10
1221 5.468663E-10
1222 5.455027E-10
1223 5.438400E-10
1224 5.418096E-10
1225 5.401075E-10
1226 5.384866E-10
1227 5.362498E-10
1228 5.337294E-10
1229 5.307729E-10
1230 5.257703E-10
1231 5.209546E-10
1232 5.170650E-10
1233 5.113223E-10
1234 5.029363E-10
1235 4.934853E-10
1236 4.827777E-10
1237 4.718339E-10
1238 4.619665E-10
1239 4.490481E-10
1240 4.337862E-10
1241 4.169303E-10
1242 3.993510E-10
1243 3.788327E-10
1244 3.636621E-10
1245 3.491450E-10
1246 3.331607E-10
1247 3.192237E-10
1248 3.067149E-10
1249 2.935923E-10
1250 2.794527E-10
1251 2.646858E-10
1252 2.479362E-10
1253 2.347190E-10
1254 2.240243E-10
1255 2.148771E-10
1256 2.092236E-10
1257 2.047304E-10
1258 2.028054E-10
1259 2.015490E-10
1260 2.441252E-04 2.004905E-10 8.212610E-07
1261 2.004905E-10
1262 1.986806E-10
1263 1.982301E-10
1264 1.967605E-10
1265 1.948214E-10
1266 1.916065E-10
1267 1.840799E-10
1268 1.767742E-10
1269 1.718816E-10
1270 1.634927E-10
1271 1.574101E-10
1272 1.524828E-10
1273 1.472407E-10
1274 1.434102E-10
1275 1.373947E-10
1276 1.307671E-10
1277 1.257198E-10
1278 1.218111E-10
1279 1.168687E-10
1280 1.097656E-10
1281 1.033981E-10
1282 9.824553E-11
1283 9.449033E-11
1284 9.170047E-11
1285 8.942612E-11
1286 8.735743E-11
1287 8.566308E-11
1288 8.396231E-11
1289 8.267322E-11
1290 8.179763E-11
1291 8.096958E-11
1292 8.028945E-11
1293 7.984124E-11
1294 7.938303E-11
1295 7.902145E-11
1296 7.871307E-11
1297 7.842309E-11
1298 7.822047E-11
1299 7.800680E-11
1300 7.767778E-11
1301 7.742182E-11
1302 7.727270E-11
1303 7.709557E-11
1304 7.687594E-11
1305 7.672518E-11
1306 7.653791E-11
1307 7.639221E-11
1308 7.626620E-11
1309 7.616467E-11
1310 7.605818E-11
1311 7.595329E-11
1312 7.587487E-11
1313 7.580633E-11
1314 7.570471E-11
1315 7.559666E-11
1316 7.546987E-11
1317 7.533126E-11
1318 7.516800E-11
1319 7.498322E-11
1320 2.441277E-04 7.475196E-11 3.062002E-07
1321 7.474189E-11
1322 7.435119E-11
1323 7.423785E-11
1324 7.418486E-11
1325 7.404805E-11
1326 7.389747E-11
1327 7.383969E-11
1328 7.379331E-11
1329 7.367873E-11
1330 7.362736E-11
1331 7.355963E-11
1332 7.347111E-11
1333 7.337145E-11
1334 7.327146E-11
1335 7.315183E-11
1336 7.302025E-11
1337 7.285638E-11
1338 7.266751E-11
1339 7.248527E-11
1340 7.234294E-11
1341 7.216381E-11
1342 7.194322E-11
1343 7.170724E-11
1344 7.146806E-11
1345 7.127295E-11
1346 7.099715E-11
1347 7.063353E-11
1348 7.023564E-11
1349 6.987296E-11
1350 6.930589E-11
1351 6.860574E-11
1352 6.795981E-11
1353 6.708354E-11
1354 6.594293E-11
1355 6.444982E-11
1356 6.318227E-11
1357 6.159285E-11
1358 5.958165E-11
1359 5.742797E-11
1360 5.450511E-11
1361 5.103054E-11
1362 4.788318E-11
1363 4.481492E-11
1364 4.235506E-11
1365 4.033064E-11
1366 3.792731E-11
1367 3.510893E-11
1368 3.299427E-11
1369 3.088599E-11
1370 2.848885E-11
1371 2.617399E-11
1372 2.361204E-11
1373 2.099245E-11
1374 1.937479E-11
1375 1.794088E-11
1376 1.660862E-11
1377 1.603638E-11
1378 1.580525E-11
1379 1.568494E-11
1380 2.441441E-04 1.559556E-11 6.387849E-08
Time = 4.70894E+00
The resulting currents are saved for plotting using tw_torus.save_current(). Here we save the real (Jr) and Imaginary (Ji) components of the response for visualization. Once all fields have been saved for plotting tw_torus.build_XDMF() to generate the XDMF descriptor files for plotting with VisIt of Paraview. This method also returns a XDMF_plot_file object, which can be used to read and interact with plot data in Python (see below).
Creating output files: oft_xdmf.XXXX.h5
Removing old Xdmf files
Removed 2 files
Found Group: thincurr
Found Mesh: icoils
# of blocks: 1
Found Mesh: smesh
# of blocks: 1
Loading sensor information
Loading flux loops from file: floops.loc
# of floops =
␂
Building element->sensor inductance matrix
Time = 0s
Building coil->sensor inductance matrix
No magnetic sensors or coils, skipping...
Real: -2.86249E-01, Imaginary: -8.96706E-06 Real: -1.22518E-01, Imaginary: 6.46086E-06
Finally we plot the current vectors on the torus showing the imaginary eddy current structure for the plasma mode. The XDMF_plot_file class provides functionality to work with the data stored in OFT plot files, including methods to generate information for 3D plotting in Python using pyvista.
Plotting data is always associated with a specific mesh, which is itself associated with a physics group. In this case ThinCurr is the physics group and the data we are interested in is stored on the surface mesh smesh. The ‘plot_data['ThinCurr’]['smesh']` is a XDMF_plot_mesh object with further functionality for accessing data.
To plot the first eigenvector we first get the pyvista plotting grid using get_pyvista_grid() and then retrieve the vertex-centered field (Ji_mode_v) using get_field()
1.9.8