Physics dataclass

documentation/source/development/add-vars.md

@@ -71,7 +71,7 @@ Here is a code snippet showing how `rmajor` is defined in `iteration_variables.p
 ```python
 ITERATION_VARIABLES = {
   ...
-  3: IterationVariable("rmajor", fortran.physics_variables, 0.1, 50.00),
+  3: IterationVariable("rmajor", "physics", 0.1, 50.00),
 ```
 
 -----------------
@@ -97,7 +97,7 @@ objective_function():
   match figure_of_merit:
   ...  
   case 1:
-        objective_metric = 0.2 * physics_variables.rmajor
+        objective_metric = 0.2 * data.physics.rmajor
 ```
 
 -----------

documentation/source/eng-models/heating_and_current_drive/RF/culham_electron_cyclotron.md

@@ -47,7 +47,7 @@ $$
 \mathtt{ecgam} = 0.25(\mathtt{ecgam1} + \mathtt{ecgam2} +\mathtt{ecgam3} + \mathtt{ecgam4})
  $$
 
-7. Calculate the current drive efficiency by dividing `ecgam` by `(dlocal * physics_variables.rmajor)`.
+7. Calculate the current drive efficiency by dividing `ecgam` by `(dlocal * data.physics.rmajor)`.
 
 $$
 \text{Current drive efficiency [A/W]} = \frac{\mathtt{ecgam}}{\mathtt{dlocal} \times R_0}

documentation/source/eng-models/heating_and_current_drive/RF/culham_lower_hybrid.md

@@ -15,9 +15,9 @@ AEA FUS 172: Physics Assessment for the European Reactor Study[^1]
 6. Calculate the parallel refractive index, `nplacc`, which is needed for plasma access. It uses the local density `dlocal` and the local magnetic field `blocal` to calculate a fraction `frac`. `nplacc` is then obtained by adding `frac` to the square root of `1.0 + frac * frac`.
 7. Calculate the local inverse aspect ratio, `epslh`, by dividing `rpenet` by `rmajor`.
 8. Calculate the LH normalised efficiency, `x`, using the formula `24.0 / (nplacc * sqrt(tlocal))`.
-9. Calculate several intermediate terms, `term01`, `term02`, `term03`, and `term04`, using different formulas involving `nplacc`, `physics_variables.zeff`, `tlocal`, `epslh`, and `x`.
+9. Calculate several intermediate terms, `term01`, `term02`, `term03`, and `term04`, using different formulas involving `nplacc`, `zeff`, `tlocal`, `epslh`, and `x`.
 10. Calculate the current drive efficiency, `gamlh`, using the formula `term01 * term02 * (1.0e0 - term03 / term04)`.
-11. Return the current drive efficiency normalised by the product of `0.1e0 * dlocal` and `physics_variables.rmajor`.
+11. Return the current drive efficiency normalised by the product of `0.1e0 * dlocal` and `rmajor`.
 
 [^1]: T. C. Hender, M. K. Bevir, M. Cox, R. J. Hastie, P. J. Knight, C. N. Lashmore-Davies, B. Lloyd, G. P. Maddison, A. W. Morris, M. R. O'Brien, M.F. Turner abd H. R. Wilson, *"Physics Assessment for the European Reactor Study"*, AEA Fusion Report AEA FUS 172 (1992)
 

documentation/source/physics-models/fusion_reactions/plasma_reactions.md

@@ -177,20 +177,20 @@ It updates the instance attributes for the cumulative power densities and reacti
 
 ### Set global physics variables | `set_physics_variables()`
 
-This method sets the required physics variables in the `physics_variables` and `physics_module` modules. It updates the global physics variables and module variables with the current instance's fusion power densities and reaction rates.
+This method sets the required physics variables on the `physics` data structure with the current instance's fusion power densities and reaction rates.
 
 #### Updates:
-- `physics_variables.pden_plasma_alpha_mw`: Updated with `self.alpha_power_density`
-- `physics_variables.pden_non_alpha_charged_mw`: Updated with `self.pden_non_alpha_charged_mw`
-- `physics_variables.pden_plasma_neutron_mw`: Updated with `self.neutron_power_density`
-- `physics_variables.fusden_plasma`: Updated with `self.fusion_rate_density`
-- `physics_variables.fusden_plasma_alpha`: Updated with `self.alpha_rate_density`
-- `physics_variables.proton_rate_density`: Updated with `self.proton_rate_density`
-- `physics_module.sigmav_dt_average`: Updated with `self.sigmav_dt_average`
-- `physics_module.dt_power_density_plasma`: Updated with `self.dt_power_density`
-- `physics_module.dhe3_power_density`: Updated with `self.dhe3_power_density`
-- `physics_module.dd_power_density`: Updated with `self.dd_power_density`
-- `physics_functions.f_dd_branching_trit`: Updated with `self.f_dd_branching_trit`
+- `pden_plasma_alpha_mw`: Updated with `self.alpha_power_density`
+- `pden_non_alpha_charged_mw`: Updated with `self.pden_non_alpha_charged_mw`
+- `pden_plasma_neutron_mw`: Updated with `self.neutron_power_density`
+- `fusden_plasma`: Updated with `self.fusion_rate_density`
+- `fusden_plasma_alpha`: Updated with `self.alpha_rate_density`
+- `proton_rate_density`: Updated with `self.proton_rate_density`
+- `sigmav_dt_average`: Updated with `self.sigmav_dt_average`
+- `dt_power_density_plasma`: Updated with `self.dt_power_density`
+- `dhe3_power_density`: Updated with `self.dhe3_power_density`
+- `dd_power_density`: Updated with `self.dd_power_density`
+- `f_dd_branching_trit`: Updated with `self.f_dd_branching_trit`
 
 -----------------------
 

documentation/source/physics-models/plasma_beta/plasma_beta.md

@@ -345,7 +345,7 @@ Found as a reasonable fit to the computed no wall limit at $f_{\text{BS}} \appro
 
 ---------
 
-#### Tholerus Relation | `calculate_beta_norm_max_thloreus()`
+#### Tholerus Relation | `calculate_beta_norm_max_tholerus()`
 
 This can be activated by stating `i_beta_norm_max = 4` in the input file.
 

documentation/source/physics-models/profiles/plasma_density_profile.md

@@ -8,7 +8,7 @@ The density profile class is organised around a central runner function that is
 
 2. The steps between the normalised points is then done by [`calculate_profile_dx()`](./plasma_profiles_abstract_class.md#calculate-the-profile-steps-in-x--calculate_profile_dx) which divides the max x-dimension by the number of points.
 
-3. The core electron and ion temperatures are claculated via [`set_physics_variables()`]()
+3. The core electron and ion temperatures are calculated via [`set_physics_variables()`]()
 
 ### Calculate core values | `set_physics_variables()`
 

examples/single_model_evaluation.ex.py

@@ -22,17 +22,15 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from process import data_structure
+from process.data_structure.physics_variables import PhysicsData
 from process.main import SingleRun
 
 # %% [markdown]
 # ## Set up
 # First, inspect a variable to check its uninitialised value:
 
 # %%
-print(
-    f"p_plasma_separatrix_mw = {data_structure.physics_variables.p_plasma_separatrix_mw}"
-)
+print(f"p_plasma_separatrix_mw = {PhysicsData.p_plasma_separatrix_mw}")
 
 # %% [markdown]
 # In order to initialise all variables in Process with their values at a given point (design parameter vector), run an evaluation input file (one with no optimisation) to initialise values in all models. The "large tokamak" regression test solution is used here.
@@ -53,9 +51,9 @@ def print_values():
     print(
         f"W frac = {single_run.data.impurity_radiation.f_nd_impurity_electron_array[13]:.3e}"
     )
-    print(f"p_plasma_rad_mw = {data_structure.physics_variables.p_plasma_rad_mw:.3e}")
+    print(f"p_plasma_rad_mw = {single_run.data.physics.p_plasma_rad_mw:.3e}")
     print(
-        f"p_plasma_separatrix_mw = {data_structure.physics_variables.p_plasma_separatrix_mw:.3e}"
+        f"p_plasma_separatrix_mw = {single_run.data.physics.p_plasma_separatrix_mw:.3e}"
     )
 
 
@@ -99,13 +97,9 @@ def run_impurities(w_imp_fracs):
         ).normalised_residual
 
         # Need to copy values
-        p_plasma_rad_mw[i] = data_structure.physics_variables.p_plasma_rad_mw.item()
-        p_plasma_separatrix_mw[i] = (
-            data_structure.physics_variables.p_plasma_separatrix_mw.item()
-        )
-        p_l_h_threshold_mw[i] = (
-            data_structure.physics_variables.p_l_h_threshold_mw.item()
-        )
+        p_plasma_rad_mw[i] = single_run.data.physics.p_plasma_rad_mw.item()
+        p_plasma_separatrix_mw[i] = single_run.data.physics.p_plasma_separatrix_mw.item()
+        p_l_h_threshold_mw[i] = single_run.data.physics.p_l_h_threshold_mw.item()
         # Need to flip sign of constraint so negative means violated
         con15[i] = -con15_value
 

process/core/caller.py

@@ -361,7 +361,7 @@ def _call_models_once(self, xc: np.ndarray, data: DataStructure):
 
         # Tight aspect ratio machine model
         if (
-            data_structure.physics_variables.itart == 1
+            data.physics.itart == 1
             and data_structure.tfcoil_variables.i_tf_sup
             != TFConductorModel.SUPERCONDUCTING
         ):