Examples
All benchmarks, examples and applications cases to be run by Kratos. Note that unit tests are in Kratos repository and NOT here
Steady inlet wind engineering CAARC problem
Author: Riccardo Tosi and Marc Núñez and Brendan Keith
Kratos version: 9.0
XMC version: Kratos default version
PyCOMPSs version: Kratos default version to run in serial, >2.8 to run with runcompss
Source files: Asynchronous and Synchronous Monte Carlo
Application dependencies: ConvectionDiffusionApplication, ExaquteSandboxApplication, FluidDynamicsApplication, LinearSolversApplications, MappingApplication, MeshingApplication, MultilevelMonteCarloApplication, StatisticsApplication
Case Specification
We solve the fluid dynamics problem of a fluid passing through a building, namely the Commonwealth Advisory Aeronautical Council (CAARC) [1]. The problem is characterized by a logarithmic and constant in time wind inlet velocity, in agreement with engineering specifications. Two different problems can be solved:
- constant in time deterministic inlet boundary conditions,
- constant in time stochastic inlet boundary conditions: reference velocity and/or roughness height are random.
To reduce the time to solution, ensemble average (see [1]) can be applied to each realization with fixed boundary conditions.
The problem can be run with two different algorithms:
- Synchronous Monte Carlo (SMC),
- Asynchronous Monte Carlo (AMC),
and by default AMC is selected. If one is interested in running SMC, it is needed to select asynchronous = false in the XMC settings (in problem_settings/parameters_xmc.json). To change the inlet boundary condition, you can set true or false the keys random_reference_velocity and random_roughness_height of Kratos settings (in problem_settings/ProjectParametersCAARC_MC_steadyInlet.json). Please observe that for running you may want to increase the number of realizations per level, the time horizon of each realization and the burn-in time (initial transient we discard when computing statistics to discard dependencies from initial conditions). All settings can be observed in the corresponding configuration file of the problem and of the algorithm.
The Quantities of Interest of the problem are the drag force, the base moment and the pressure field on the building surface and their time-averaged counterparts. Statistical convergence is assessed for the time-averaged drag force.
To run the examples, the user should go inside the source folder and run the run_mc_Kratos.py Python file. In case one wants to use PyCOMPSs, the user should execute run.sh from inside the source folder.
Results
The velocity and pressure fields evolution of the problem are shown next.
An example of power sums and h-statistics of both time averaged and time series drag force, base moment and pressure field can be found here.
A literature comparison has been performed to ensure the correctness and accuracy of our solution; we refer to [1] for details.
Refrences
[1] Tosi, R., Núñez, M., Pons-Prats, J., Principe, J. & Rossi, R. (2022). On the use of ensemble averaging techniques to accelerate the Uncertainty Quantification of CFD predictions in wind engineering. Journal of Wind Engineering and Industrial Aerodynamics. https://doi.org/10.1016/j.jweia.2022.105105