5. Capabilities¶
Version 3.0 of the HydroUQ app was released on November 30, 2023. 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.)
5.1. UQ (Uncertainty Quantification and Optimization Options)¶
Water Event Selection: Users are provided with multiple paths for water borne hazard generation:
A. 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.
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.
Note
Support for the running computation to be performed on a TACC high-performance computer, e.g. Frontera or Lonestar6, is provided through DesignSafe for all but the methods indicated with a star (*).
5.2. FEM (Computational Model Specification)¶
OpenSees
FEAPpv
Python
Custom
SurrogateGP
None
Multiple models
5.3. RV (Random Variable Options)¶
Inspect 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.
5.4. EDP (Outputs from Computational Models)¶
Scalar quantities of interest
Vector quantities of interest
5.5. 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