Merge branch 'master' of https://github.com/KratosMultiphysics/Examples

Author: rubenzorrilla

co_simulation/CoSimulation_uml.png

@@ -4,16 +4,16 @@
 
 **Kratos version:** 8.1
 
-**XMC version:** 2.0
+**XMC version:** Kratos default version
 
-**PyCOMPSs version:** 2.7
+**PyCOMPSs version:** Kratos default version to run in serial, >2.8 to run with `runcompss`
 
 **Source files:** [Asynchronous Monte Carlo and Asynchronous Multilevel Monte Carlo](source)
 
 **Application dependencies:** `CompressiblePotentialFlowApplication`, `LinearSolversApplications`, `MappingApplication`, `MeshingApplication`, `MultilevelMonteCarloApplication`
 
 ## Case Specification
-We solve the [compressible potential flow problem](https://github.com/KratosMultiphysics/Kratos/blob/master/applications/CompressiblePotentialFlowApplication/python_scripts/potential_flow_analysis.py) around an airfoil NACA0012. The problem is characterized by stochastic angle of attack <img src="https://render.githubusercontent.com/render/math?math=\alpha\sim\mathcal{N}(5.0,0.05)"> and stochastic Mach number <img src="https://render.githubusercontent.com/render/math?math=M_{\infty}\sim\mathcal{N}(0.3,0.003)">.
+We solve the [compressible potential flow problem](https://github.com/KratosMultiphysics/Kratos/blob/master/applications/CompressiblePotentialFlowApplication/python_scripts/potential_flow_analysis.py) around an airfoil NACA0012. The problem is characterized by stochastic angle of attack <img src="https://render.githubusercontent.com/render/math?math=\alpha\sim\mathcal{N}(5.0,0.05)"> and stochastic Mach number <img src="https://render.githubusercontent.com/render/math?math=M_{\infty}\sim\mathcal{N}(0.3,0.003)">. The problem is presented in our work [1].
 
 The problem can be run with four different algorithms:
 
@@ -33,7 +33,18 @@ To run the examples, the user should go inside the folder-algorithm of interest
 ## Results
 
 The pressure coefficient for the problem is shown next.
-![cpf_pressurecoeff](data/cpf_pressure.png)
+
+<img src="data/cpf_pressure.png" alt="pressure coefficient" width="500"/>
+
+The pressure coefficient risk measure <img src="https://render.githubusercontent.com/render/math?math=\mathbb{E}[C_p] \pm \sigma[C_p]"> is shown next.
+
+<img src="data/cpf_expvalue_pm_stddeviation.png" alt="pressure coefficient risk measure" width="500"/>
 
 The power sums and the h-statistics of both the lift coefficient and the pressure coefficient can be found [here](source/power_sums_outputs).
-The lift coefficient expected value is consistent with literature results [M. Davari, R. Rossi, P. Dadvand, I. López, R. W. (2019). A cut finite element method for the solution of the full-potential equation with an embedded wake. Comput Mech, 63(5), 821–833. https://doi.org/https://doi.org/10.1007/s00466-018-1624-3].
+The lift coefficient expected value is consistent with literature results [2].
+
+## References
+
+[1] Tosi, R., Amela, R., Badia, R., & Rossi, R. (2021). A parallel dynamic asynchronous framework for Uncertainty Quantification by hierarchical Monte Carlo algorithms. Journal of Scientific Computing, 89(28), 25. https://doi.org/10.1007/s10915-021-01598-6
+
+[2] M. Davari, R. Rossi, P. Dadvand, I. López, R. W. (2019). A cut finite element method for the solution of the full-potential equation with an embedded wake. Comput Mech, 63(5), 821–833. https://doi.org/https://doi.org/10.1007/s00466-018-1624-3

co_simulation/cosim_coupling.png

@@ -0,0 +1,13 @@
+{
+    "properties" : [{
+        "model_part_name" : "model",
+        "properties_id"   : 1,
+        "Material"        : {
+            "Variables"        : {
+                "DENSITY"       : 1.225
+            },
+            "Tables"           : {}
+        }
+    }]
+}
+

co_simulation/detached_interface.png

@@ -14,6 +14,9 @@
             "input_type"     : "mdpa",
             "input_filename" : "problem_settings/naca0012Mesh4"
         },
+        "material_import_settings": {
+            "materials_filename": "problem_settings/materials.json"
+        },
         "formulation"  : {
             "element_type": "compressible"
         },

multilevel_monte_carlo/use_cases/compressible_potential_flow/README.md

@@ -14,6 +14,9 @@
             "input_type"     : "mdpa",
             "input_filename" : "problem_settings/CPS_MONTECARLO_MeshInterpError1e-2"
         },
+        "material_import_settings": {
+            "materials_filename": "problem_settings/materials.json"
+        },
         "formulation"  : {
             "element_type": "compressible"
         },

multilevel_monte_carlo/use_cases/compressible_potential_flow/data/cpf_expvalue_pm_stddeviation.png

@@ -14,6 +14,9 @@
             "input_type"     : "mdpa",
             "input_filename" : "problem_settings/CPS_MONTECARLO_MeshInterpError5e-3"
         },
+        "material_import_settings": {
+            "materials_filename": "problem_settings/materials.json"
+        },
         "formulation"  : {
             "element_type": "compressible"
         },

multilevel_monte_carlo/use_cases/compressible_potential_flow/source/problem_settings/materials.json

@@ -1,5 +1,5 @@
 {
-    "hessian_metric": {
+    "metric": {
 		"hessian_strategy_parameters"              :{
 				"metric_variable"				           : ["VELOCITY_X,VELOCITY_Y"],
 				"estimate_interpolation_error"     : false,
@@ -11,7 +11,7 @@
 		"anisotropy_remeshing"              : true,
 		"enforce_anisotropy_relative_variable" : false
     },
-    "refinement_mmg" : {
+    "remeshing" : {
         "initialize_entities"              : false,
         "echo_level"                       : 0
     }