Merge pull request #449 from fusion-energy/fixing-task-descriptions

adding task descriptions

Author: shimwell

tasks/task_10_activation_transmutation_depletion/README.md

@@ -4,11 +4,13 @@ Please allow 20 minutes for this task.
 
 Expected outputs from this task are also in the [presentation](https://slides.com/neutronics_workshop/neutronics_workshop#/15).
 
-In this task you will be using OpenMC to simulate transmutations / activation / depletion within a material under neutron irradiation.
-
+In this task you will use OpenMC to simulate transmutation, activation and depletion of materials under neutron irradiation. The examples progress from a simple flux-spectrum-based depletion (no transport required) through to coupled transport-depletion simulations with pulsed irradiation schedules.
 
 **Learning Outcomes**
 
-- OpenMC can be used to track the build up and decay of activation products.
-- The build up of unstable activated product starts to saturate at around five halflives, at this point the rate of creation of new activation products approaches the rate of decay.
-- OpenMC can track the impact of material evolution on a tally. A material under irradiation transmutations and changes over time. This impacts the movement of neutron through the material which in turn influences tha tally result. Tritium Breeding Ratio tallies tend to decrease as the lithium 6 is burnt-up.
+- OpenMC can perform depletion using a precomputed multigroup flux spectrum, similar to inventory codes like FISPACT, ORIGEN and ALARA. This is fast and requires no geometry.
+- OpenMC can also perform coupled transport-depletion where the neutron flux is recalculated as the material composition evolves.
+- Activation products build up during irradiation and start to saturate at around five half-lives, when the rate of creation approaches the rate of decay.
+- Activity, decay heat and contact dose rate can be extracted from depleted materials.
+- Material evolution during irradiation affects tally results. For example, Tritium Breeding Ratio decreases as lithium-6 is burnt up.
+- Pulsed irradiation schedules can be modelled to simulate realistic reactor operating conditions.

tasks/task_11_CSG_shut_down_dose_tallies/README.md

@@ -6,7 +6,7 @@ In this task you will simulate shutdown dose rates on CSG (Constructive Solid Ge
 
 The D1S method is the recommended approach. It folds decay photon production directly into a single neutron transport run and computes dose rates as a post-processing step, making it fast to vary the irradiation schedule without re-running the simulation.
 
-The R2S examples in this task (Python scripts) run a neutron transport simulation, perform material activation/depletion, construct gamma source terms from the activated materials, then run a separate photon transport simulation. These examples are planned to be improved and converted to notebooks in a future update.
+The R2S examples run a neutron transport simulation, perform material activation/depletion, construct gamma source terms from the activated materials, then run a separate photon transport simulation.
 
 **Learning Outcomes**
 

tasks/task_12_detectors_examples/README.md

@@ -0,0 +1,11 @@
+# Task Introduction
+
+In this task you will use OpenMC to simulate time-resolved neutron detection. The examples demonstrate how to use time filters to tally neutron interactions as a function of time, which is relevant for modelling pulsed neutron sources, time-of-flight measurements, and detector response studies.
+
+The examples progress from a simple time-filtered tally, to including reflective boundary conditions that allow neutrons to bounce and be detected at later times, and finally to combining time and energy filters for a more detailed detector response.
+
+**Learning Outcomes**
+
+- Time filters can be applied to tallies to resolve neutron interactions as a function of time after a pulse.
+- Reflective boundaries cause neutrons to persist in the system, producing signals at later times.
+- Time and energy filters can be combined to produce time-energy resolved spectra, similar to time-of-flight detector measurements.

tasks/task_13_volume_calculations/README.md

@@ -1,11 +1,9 @@
 # Task Introduction
 
-In this task you will use OpenMC to calculate the volumes of cells in the geometry.
-
-Volumes of cells are needed for some types of simulation (e.g. depletion)
+In this task you will use OpenMC to calculate the volumes of cells in CSG and DAGMC geometries. Accurate cell volumes are needed for depletion simulations and for normalising tally results to physical quantities (e.g. dose rate per unit volume).
 
 **Learning Outcomes**
 
-- The volume of a cell or a material can be found using the stochastic volume method.
-- Volume found using stochastic has an accompanied by a uncertainty.
+- The volume of a cell or material can be found using the stochastic volume method in OpenMC.
+- Stochastic volume calculations have an associated uncertainty that decreases with more samples.
 - Volumes of cells in DAGMC geometry can be calculated using pydagmc.

tasks/task_14_variance_reduction/README.md

@@ -1,18 +1,14 @@
 # Task Introduction
 
-Currently OpenMC supports two types of variance reduction (survival biasing and weight windows).
-A detailed description of each method can be found in the [documentation](https://docs.openmc.org/en/stable/methods/neutron_physics.html?highlight=survival#variance-reduction-techniques).
+Variance reduction techniques are essential for efficiently simulating deep-penetration shielding problems where analogue Monte Carlo would require prohibitively many particles. In this task you will use OpenMC to apply survival biasing, weight windows and FW-CADIS to shielding and sphere problems.
 
-OpenMC also supports methods of generating weight windows including the [Magic Method and FW-CADIS](https://docs.openmc.org/en/stable/methods/variance_reduction.html?highlight=magic)
+A detailed description of each method can be found in the [OpenMC documentation](https://docs.openmc.org/en/stable/methods/neutron_physics.html?highlight=survival#variance-reduction-techniques). OpenMC also supports methods of generating weight windows including the [Magic Method and FW-CADIS](https://docs.openmc.org/en/stable/methods/variance_reduction.html?highlight=magic).
 
-The notebooks contain all the methods for completeness, however ff you just have time to learn one method then I would recommend the FW-CADIS approach.
+The examples cover all methods for completeness, however if you only have time to learn one method then the FW-CADIS approach is recommended.
 
-The workshop contains the following variance reduction examples:
+**Learning Outcomes**
 
-| Filename | variance reduction technique | geometry | mesh type |
-|---|---|---|---|
-| 1_shielded_room_survival_biasing.py | survival_biasing | shielded bunker | RegularMesh | Flux map | air space and concrete |
-| 2_shielded_room_single_ww.ipynb | weight windows | sphere | RegularMesh | air space and concrete |
-| 3_sphere_iterative_per_run_ww.py | weight windows | cube | RegularMesh | Water |
-| 4_sphere_iterative_per_batch_ww.py | weight windows | sphere | SphericalMesh | concrete |
-| 5_shielded_room_single_fw_cadis.py | survival_biasing | shielded bunker | RegularMesh | Flux map | air space and concrete |
+- Survival biasing prevents particles from being killed by absorption, instead reducing their weight, which improves statistics in deep-penetration problems.
+- Weight windows control particle weights across the geometry, splitting particles in important regions and killing them in unimportant regions.
+- Weight windows can be generated iteratively (per-run or per-batch) using the Magic method.
+- FW-CADIS (Forward-Weighted Consistent Adjoint Driven Importance Sampling) automates weight window generation using an adjoint flux calculation to optimise for a specific tally.

tasks/task_21_design_task/README.md

@@ -1,3 +1,9 @@
 # Task Introduction
 
-The goal of this task is to explore the trade space and apply some of the neutronics analysis learned during the course.
+In this task you will apply the neutronics analysis techniques learned during the course to explore a design trade space. This is an open-ended exercise where you optimise a fusion reactor blanket design by varying geometry and material parameters to achieve target performance metrics such as Tritium Breeding Ratio, shielding effectiveness, and heat deposition.
+
+**Learning Outcomes**
+
+- Neutronics simulations can be used to perform parametric design studies by sweeping over geometry and material parameters.
+- Multiple competing objectives (e.g. tritium breeding vs shielding) must be balanced when designing a fusion blanket.
+- Automated parameter sweeps allow the design space to be explored efficiently.