Photon Transport Simulation with Two Layers of Material & Unstructured Mesh #27
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Co-authored-by: Paul Wilson <[email protected]>
- Reading total strengths directly from Source_Mesh_Reader instead of writing a new list in this script
- Brought the MeshBase object inside the loop over the energy bounds
Assign material library path to new function and delete unused lines
These two files can be imported into a neutron or photon transport problem
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| # Create geometry | ||
| #Spherical shell: | ||
| def make_spherical_shell(inner_radius_W, outer_radius_W, inner_radius_C, M_1, M_2): |
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docstrings here would help be clear about what everything is doing
| Cells = [Void, W_Shell, C_Shell] | ||
| geometry = openmc.Geometry(Cells) | ||
| geometry.export_to_xml() | ||
| return geometry, Void, W_Shell, C_Shell, Cells |
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seems like you are returning more than necessary since Cells contains W_Shell and C_Shell, and geometry contains Cells
| C_Shell = openmc.Cell(fill=M_2, region=S_C_3) | ||
| Cells = [Void, W_Shell, C_Shell] | ||
| geometry = openmc.Geometry(Cells) | ||
| geometry.export_to_xml() |
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Probably don't want to export here - separation of concerns points to putting computing and input/output in different methods.
| @author: Anupama Rajendra | ||
| """ | ||
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| import openmc |
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Not clear that we need to import openmc here, if this method is only ever used when importing into another script that also imports openmc
| S_W_1= openmc.Sphere(r=inner_radius_W) #sphere of radius 1000cm | ||
| inside_W_sphere_1 = -S_W_1 | ||
| outside_W_sphere_1 = +S_W_1 | ||
| S_W_2 = openmc.Sphere(r=outer_radius_W, boundary_type='vacuum') | ||
| inside_W_sphere_2 = -S_W_2 | ||
| outside_W_sphere_2 = +S_W_2 | ||
| S_W_3 = outside_W_sphere_1 & inside_W_sphere_2 #filled with specified material |
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S_W_1 and S_W_2 are surfaces, but S_W_3 is a region - better variable names will make this more readable.
It's also not clear that you need to define the intermediate variables inside_... and outside_.... If S_W_1 and S_W_2 (and S_C_1 below, etc) were named better, then the OpenMC notation with - and + might just be clear
| S_W_1= openmc.Sphere(r=inner_radius_W) #sphere of radius 1000cm | |
| inside_W_sphere_1 = -S_W_1 | |
| outside_W_sphere_1 = +S_W_1 | |
| S_W_2 = openmc.Sphere(r=outer_radius_W, boundary_type='vacuum') | |
| inside_W_sphere_2 = -S_W_2 | |
| outside_W_sphere_2 = +S_W_2 | |
| S_W_3 = outside_W_sphere_1 & inside_W_sphere_2 #filled with specified material | |
| inner_W_sphere= openmc.Sphere(r=inner_radius_W) #sphere of radius 1000cm | |
| outer_W_sphere = openmc.Sphere(r=outer_radius_W, boundary_type='vacuum') | |
| W_region = +inner_W_sphere & -outer_W_sphere |
| tstt = file['tstt'] | ||
| elements = tstt['elements'] | ||
| tet_four = elements['Tet4'] | ||
| tags = tet_four['tags'] | ||
| sd = tags['source_density'][:] | ||
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| # Write source density to a separate file for each mesh | ||
| with open(f'source_density_{file_index + 1}.txt', 'w') as source: | ||
| for tet_element in sd: | ||
| source.write(' '.join(map(str, tet_element)) + '\n') | ||
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| # Calculate summed (and individual mesh) strengths for each photon group | ||
| summed_strengths = [] | ||
| strengths_list = [] | ||
| for group in range(photon_groups): | ||
| total_strengths = np.sum(sd[:, group]) # Sum over all mesh elements | ||
| summed_strengths.append(total_strengths) | ||
| strengths = sd[:,group] | ||
| strengths_list.append(strengths) |
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I recommend putting this in a method that does everything you need for a single file. That kind of modularity enhances readability. You might also want to separate the functions of reading from a file, processing the data, and writing to files, so methods like extract_source_data, arrange_data, write_source_density
| summed_strengths = [] | ||
| strengths_list = [] |
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make these numpy arrays to start with?
| total_strengths = np.sum(sd[:, group]) # Sum over all mesh elements | ||
| summed_strengths.append(total_strengths) | ||
| strengths = sd[:,group] | ||
| strengths_list.append(strengths) |
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How is strengths_list different from sd? sd is a 2-D numpy array of size (num_elements, num_groups). It looks like strengths_list is a list of lists with num_group lists, each of length num_elements.
Also - you don't appear to use any of the variables made in this loop?
| flux_spectrum = sp.get_tally(id=2) | ||
| mesh=sp.meshes[1] | ||
| # Get the reshaped tally data | ||
| tally_data_reshaped = flux_spectrum.get_reshaped_data(value='mean') | ||
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| flux_sum_en = tally_data_reshaped.sum(axis=(0,1,3,4,5)) | ||
| #Vitamin-J energy filter: | ||
| e_filter = flux_spectrum.filters[2] | ||
| #Lower bounds of the energy bins | ||
| e_filter_lower = e_filter.bins[:, 0] |
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A lot of magic numbers here including tally ID, mesh number, axes for sum, etc... more readable if these are named as variables, and might make sense if they lived in a function that took some of these as arguments.
| # Plot flux spectrum | ||
| fix, ax = plt.subplots() | ||
| ax.loglog(e_filter_lower, flux_sum_en, drawstyle='steps-post') | ||
| ax.set_xlabel('Energy [eV]') | ||
| ax.set_ylabel('Flux [photon-cm/source]') | ||
| ax.grid(True, which='both') | ||
| plt.savefig('Photon_flux_vs_energy.png') | ||
| plt.show() | ||
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| mesh_data = tally_data_reshaped.sum(axis=(0,2,3,4,5)) | ||
| vtk = mesh.write_data_to_vtk(filename="Photon_Flux.vtk", datasets={"mean":mesh_data.flatten()}) |
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separation of concerns: make different functions/methods for reading data, processing data, writing data, and plotting data.
| OpenMC_W = make_W(element_1, OpenMC_SF) | ||
| OpenMC_C = make_C(element_2, OpenMC_SF) |
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| OpenMC_W = make_W(element_1, OpenMC_SF) | |
| OpenMC_C = make_C(element_2, OpenMC_SF) | |
| materials = [] | |
| for material_id, element in enumerate(elements): | |
| materials.append(make_element(element, material_id+1, OpenMC_SF)) |
This model currently uses the relative source intensities between different photon groups as the source strengths (and samples equally between individual mesh elements for any given energy group). However, it should be possible to add spatial variation of source intensity onto the MeshSpatial distribution.