# 5. Capabilities¶

Version 4.0.0 of the HydroUQ app was released on **April 1st, 2024**. The following lists the functionality available in this current version. (Note: New features and fixes in this release are marked blue in the following list of features.)

Water Event Selection: Users are provided with multiple paths for water borne hazard generation:

Generate/record integrated loads and point pressure measurements by creating and running a CFD model on DesignSafe.

Run GeoClaw, a widely used shallow-water solver vetted for tsunamis / storm surges, via the graphical user interface.

Define and adjust prebuilt, digital twin wave-makers (1D / 2D pistons, pumps, gravity head).

For advanced users, full authority is provided to input hydrodynamic files from tools of their choice.

Structural Model: Defines the structural modeling approach and returns the scripts required to perform the response simulation. One or more models can be assigned to a workflow. Using more than one model allows for benchmarking and epistemic uncertainty analysis. The following options are available:

Provide your own OpenSees model in Tcl or Python format.

Provide a Python script that prepares a structural model and performs the response simulation.

Automatically generate an idealized shear column model in OpenSees from basic building information.

Response Simulation: Defines the analysis options that will be used to perform the numerical simulation, e.g., time integration strategy, convergence criteria, and damping options. The user-specified modeling tool is used to perform the simulation and collect the requested response quantities.

## 5.1. UQ (Uncertainty Quantification and Optimization Options)¶

Uncertainty Quantification: Samples the prescribed random input variables and obtains realizations of the outputs by executing the workflow with each input realization from the generated sample. The underlying UQ engines let you leverage the following techniques in your research:

Forward propagation Dakota SimCenterUQ: Define a set of random input parameters and perform simulations to obtain a corresponding sample of output parameters and their statistics.

Sensitivity analysis Dakota SimCenterUQ: Measure the influence of the uncertainty in each input on the uncertainty of outputs.

Reliability analysis Dakota SimCenterUQ: Algorithms to estimate the probability of exceeding a failure surface.

## 5.2. SIM (Structural Model)¶

Multi-degree-of-freedom (MDOF) model

MDOF-LU

OpenSees

Custom Python Script (customPy)

Multiple Models

## 5.3. EVT (Event Selection)¶

Style

General Event

Broad functionality for designing custom events with custom bathymetry, structures, initial conditions, etc.

Digital Twins

Prevalidated digital twins of wave-flume experimental facilities

Available wave-flume twins

Oregon State University Large Wave Flume (OSU LWF)

Waseda University’s Tsunami Wave Basin (WU TWB)

Available wave-maker twins

1D Piston

Vacuum Chamber Reservoir

Dam-break

Numerical Methods

OpenFOAM

Computational Fluid Dynamics

Partially deprecated functionality for allowing inlets for GeoClaw / generic shallow-water equation solutions in OpenFOAM

^{2}Available for running on TACC HPC

FOAMySees

Computational Fluid Dynamics using OpenFOAM

Computational Structural Dynamics using OpenSees

Two-way coupled using Precice library

Available for running on TACC HPC

Material Point Method (ClaymoreUW MPM)

^{1}Unified debris-fluid-structure-soil interaction simulations using Material Point Method (MPM)

Multi-GPU accelerated

Available for running on TACC HPC

GeoClaw (

**Returning Soon**)^{2}Shallow-water solver for tsunamis and storm surges

Available for running on DesignSafe

Available for running on local machine

Available for running on TACC HPC

Note

^{1} ClaymoreUW MPM numerical method currently only available as a standalone tool in the `Tools`

header-ribbon. Soon to be introduced into the full workflow.

^{2} GeoClaw functionality from HydroUQ v1.0 is partially deprecated in the current version. It is to be fully reintroduced in the near future.

## 5.4. FEM (Computational Model Specification)¶

OpenSees

Python

Custom

Multiple models

## 5.5. RV (Random Variable Options)¶

Inspect probability distribution function (PDF) of RV

Distributions available:

^{1}Normal

Lognormal

Beta

Uniform

Weibull

Gumbel

Continuous

^{2}Exponential

^{3}Discrete

^{3}Gamma

^{3}Chi-squared

^{3}Truncated exponential

^{3}

Note

^{1}: For SimCenterUQ and UCSD algorithms only, the RVs can be defined through any of these options - parameters, moments, or a dataset.
^{2}: Available for Optimization routines in Dakota only.
^{3}: Available in SimCenterUQ and UCSD only.

## 5.6. EDP (Outputs from Computational Models)¶

Available sets of EDPs:

Standard Earthquake EDPs

Peak Inter-story Drift (PID)

Peak Floor Acceleration (PFA)

Peak Roof Displacement (PRD)

Standard Tsunami EDPs`

Peak force / pressure / wave-velocity / wave-height / inter-story drift (PID) / roof displacement (PRD)

Total impulse / wave-duration / wave momentum-flux

Average wave velocity / wave height

User Defined EDPs

Define EDPs in the additional input files

Populate response parameter names in the GUI

Define a post-processing script to create the

`results.out`

file of appropriate format (single line where each value corresponds to a response parameter) using only the FEM simulation output.Use the output of the FEM simulation to calculate the EDPs

Format of EDPs:

Scalar quantities of interest

Vector quantities of interest

## 5.7. RES (Summary and Visualization of UQ Analysis Results)¶

Summary statistics of outputs displayed

Mean

Standard deviation

All output values presented in the spreadsheet

Update the chart by clicking on spreadsheet columns

Output values visualized in the interactive chart

Scatter plot

Histogram

Cumulative distribution

Inspect points on chart

Spreadsheet save options

Save Table

Save Columns Separately (Useful after Bayesian updating, the posterior samples can later be directly loaded in HydroUQ)

Save RVs (Useful for surrogate model training)

Save QoIs (Useful for surrogate model training)

Save Surrogate Predictions (Only for the surrogate model results)

Visualization of surrogate modeling (GP) results

Goodness-of-fit measures

90% confidence interval and prediction interval

Save GP model

Visualization of PLoM training results

PCA representation error plot

Diffusion maps eigenvalue plot