HPC Model Review: Difference between revisions

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HPC is an unconditionally stable 2D solution scheme that is mass conservative. This can result in HPC “hiding” poor data or model setup. Modellers must take measures to ensure the quality of their HPC models for commercial uses, by taking all reasonable steps to correct or at the very least understand areas of poor representation in their models.<br>
 
= TUFLOW Log FilesFile (*.tlf, *hpc.tlf) =
The first step of reviewing a HPC model is no different to reviewing a TUFLOW Classic model. Start by opening the TUFLOW log file (*.tlf) and confirm at the bottom of the file, that the model run finished successfully by observing "Simulation FINISHED" and that the final mass error reported is acceptable as shown below:<br>
 
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Though HPC is mass conserving it is still important to review this Mass Error, as it can still occur when coupling HPC with 1D elements in either the 1D/2D linking, or in the 1D itself.<br>
A 'healthy' model will usually report up to ±1% mass error. The total model mass error can also be observed in the TUFLOW Summary File (*.tsf) output as oppose to the tlf file. <br>
 
The new *hpc.tlf file can also be used to review the dt, controlling number values and the water volume in the model. However, this may be easier to review using the hpc.dt.csv output.<br>
= HPC TUFLOW Log File (*hpc.tlf) =
The.hpc.tlf log file will record “repeated timestep” if there is a significant change in timestep. It is inevitable to have repeated timesteps, if a model has a sudden change in rainfall input from one timestep to the next, or has a warmup period with small flow rate before a large inflow. However, if there is a high occurrence of repeated timesteps when the boundary inflows are smooth, this could be an indicator of model instability.<br>
 
The new *hpc.tlf file can also be also used to review the dt, controlling number values and the water volume in the model. However, this may be easier to review using the hpc.dt.csv output.<br>
 
= HPC dt Time Series Output (*.hpc.dt.csv) =