R2D Requirements

R2D is the UI for a regional simulation. It uses rWhale to run the workflow. The requirements from R2D come from many components.

General Requirements

Requirements - R2D

#

Description

Source

Priority

Status

Implementation

R2D

Ability to perform regional simulation allowing communities to evaluate resilience and perform what-if types of analysis for natural hazard events

GC

M

InProgress

1.1.1.1.3-1.1.1.1.4, 1.1.3.4.6, 1.1.4.2.1-1.1.4.2.5, 1.1.4.3.1-1.1.4.3.2

R2D.1

Include Various Hazards

GC

M

InProgress

R2D.1.1

Ability to perform simulations for ground shaking due to earthquakes using methods defined in EL1

GC

M

Implemented

R2D UM 3.1

R2D.1.2

Ability to perform simulations for wave action due to earthquake induced tsunami using methods defined in HL1

GC

M

InProgress

1.1.3.3.2-1.1.3.3.3, 1.1.3.5.2

R2D.1.3

Ability to perform simulations for wind action due to hurricane using methods defined in WL1

GC

M

InProgress

1.1.3.3.2-1.1.3.3.3, 1.1.3.5.1-1.1.3.5.2

R2D.1.4

Ability to perform simulations for wave action due to hurricane storm surge using methods defined in HL1

GC

M

InProgress

1.1.3.3.2-1.1.3.3.3, 1.1.3.5.1-1.1.3.5.2

R2D.1.5

Ability to perform multi-hazard simulations: wind + storm surge + rain + wind and water borne debris

GC

M

InProgress

1.1.3.3.2-1.1.3.3.3, 1.1.3.5.1-1.1.3.5.2

R2D.1.6

Ability to utilize machine learning ensemble techniques in hazard simulation

GC

M

R2D.1.7

Ability to incorporate surrogate models in hazard simulation

SP

M

R2D.1.8

Ability to incorporate multi-scale models in hazard simulation

SP

M

InProgress

1.1.3.3.2, 1.1.3.3.5

R2D.1.9

Ability to incorporate ground deformation hazards for pipes, roadways, and other infrastructure

SP

M

InProgress

1.1.3.4.4, R2D UM 3.1.5

R2D.2

Include Different Asset Types

GC

M

InProgress

R2D.2.1

Ability to incorporate building assets

GC

M

Implemented

R2D UM 2.4.1

R2D.2.1.1

Ability to incorporate multi-fidelity building model asset descriptions

GC

M

InProgress

1.1.3.3.1

R2D.2.2

Ability to incorporate transportation networks

GC

M

InProgress

1.1.3.1.3, 1.1.3.4.1, 1.1.3.4.3, 1.1.3.5.3, R2D UM 2.4.3

R2D.2.3

Ability to incorporate utility networks

GC

M

InProgress

1.1.3.1.4, 1.1.3.3.5, 1.1.3.4.1-1.1.3.4.7

R2D.2.3.1

Methods to overcome national security issues with certain utility data

GC

M

InProgress

1.1.3.4.5

R2D.2.4

Ability to incorporate surrogate models in asset modeling

SP

M

R2D.3

Include Different Analysis options

GC

M

Implemented

R2D.3.1

Ability to include multi-scale nonlinear models

GC

M

Implemented

R2D UM 2.6.1 , R2D UM 2.7.1

R2D.4

Include Different Damage & Loss options

GC

M

InProgress

R2D.4.1

Ability to include building-level earthquake damage and loss assessment from HAZUS

SP

M

Implemented

R2D UM 2.7.1.3 , R2D Example 1

R2D.4.2

Ability to include high-resolution earthquake damage and loss assessment for buildings from FEMA P58

SP

M

Implemented

R2D UM 2.8.1

R2D.4.3

Ability to include building-level wind damage and loss assessment from HAZUS

SP

M

Implemented

PELICUN UM 2.4

R2D.4.4

Ability to include building-level water damage and loss assessment from HAZUS

SP

M

Implemented

PELICUN UM 2.4

R2D.4.5

Ability to include earthquake damage and loss assessment for transportation networks from HAZUS

SP

M

Implemented

R2D Example 14

R2D.4.6

Ability to include earthquake damage and loss assessment for buried pipelines from HAZUS

SP

M

InProgress

1.1.3.4.3

R2D.4.7

Ability to include earthquake damage and loss assessment for power lines from HAZUS

SP

M

InProgress

1.1.3.4.3

R2D.4.8

Ability to include high-resolution wind damage and loss assessment for buildings

SP

M

InProgress

1.1.3.5.1

R2D.4.9

Ability to include high-resolution water damage and loss assessment for buildings

SP

M

InProgress

1.1.3.5.2

R2D.4.10

Ability to include high-resolution damage and loss assessment for transportation networks

SP

M

InProgress

1.1.3.5.3

R2D.4.11

Ability to include high-resolution damage and loss assessment for buried pipelines

SP

M

InProgress

1.1.3.5.4

R2D.5

Include Different Response/Recovery options

GC

M

InProgress

R2D.5.1

Response/Recovery options for households

SP

M

InProgress

1.1.4.2.3

R2D.5.2

Response/Recovery options for infrastructure

SP

M

InProgress

1.1.4.2.4

R2D.5.3

Response/Recovery options for business operations

SP

M

InProgress

1.1.4.2.5

R2D.5.4

Response/Recovery and Effect on Environment

SP

M

InProgress

1.1.4.3.1

R2D.5.4.1

CO2 emissions from demolition and repair

SP

M

InProgress

1.1.4.3.1

R2D.6

Present results using GIS so communities can visualize hazard impacts

GC

M

Implemented

R2D.6.1

Ability to use popular ArcGIS for visualization

SP

M

Implemented

R2D.6.2

Ability to include open-source ArcGIS alternatives

SP

P

Implemented

R2D.6.3

Ability to capture uncertainty of results in visualization

SP

P

InProgress

1.1.3.6.4

R2D.6.4

Features to visualize environmental impact

SP

P

R2D.7

Software Features

GC

M

InProgress

R2D.7.1

Ability to include a formal treatment of uncertainty and randomness

GC

M

Implemented

R2D UM 2.9

R2D.7.2

Ability to utilize HPC resources in regional simulations that enables repeated simulation for stochastic modeling

GC

M

Implemented

R2D UM Run at DesignSafe

R2D.7.3

Ability to use a tool created by linking heterogeneous array of simulation tools to provide a toolset for regional simulation

GC

M

Implemented

R2D DM 2.3

R2D.7.4

Provide open-source software for developers to test new data and algorithms

GC

M

Implemented

R2D DM 1

R2D.7.5

Ability of stakeholders to perform simulations of different scenarios that aid in planning and response after damaging events

GC

M

Implemented

R2D UM 2.3.3

R2D.7.7

Ability to explore different strategies in community development pre-event early response and post event through long term recovery

GC

P

InProgress

1.1.4.2.1-1.1.4.2.5

R2D.7.8

Ability to use system that creates and monitors real-time data updates models incorporates crowd sourcing technologies and informs decision makers

GC

P

R2D.7.9

Ability to use sensor data to update models for simulation and incorporate sensor data into simulation

GC

P

R2D.7.10

Ability to include latest information and algorithms (i.e. new attenuation models building fragility curves demographics lifeline performance models network interdependencies indirect economic loss)

GC

D

InProgress

1.1.3.4.6

R2D.7.11

Incorporate programs that can address lifeline network disruptions and network interdependencies

GC

M

InProgress

1.1.3.4.6

R2D.7.12

Application to provide common SimCenter research application requirements listed in CR (not already listed above)

GC

M

InProgress

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

Earthquake Loading Requirements

Requirements - EL

#

Description

Source

Priority

Status

Implementation

EL.1

Regional Scale Earthquake Hazard Simulation Options

_

_

_

_

EL.1.1

Coupling of multi-scale nonlinear models from the point of rupture through rock and soil into structure

_

_

_

_

EL.1.1.1

Replacement of empirical linear models with multi-scale nonlinear models

GC

D

_

_

EL.1.1.2

Include both multi-scale and multi-phase (account for liquefaction)

GC

M

InProgress

1.1.3.3.3, 1.1.3.3.5

EL.1.1.3

Interface between asset and regional simulations using site response method

SP

M

Implemented

R2D UM 2.3.5

EL.1.1.4

Interface between asset and regional simulations using DRM method

SP

M

InProgress

_

EL.1.2

Method to include both the intra-event residual and inter-event residual in simulating spatial correlated ground motion intensity measures with multiple correlation model options. Select site-specific ground motions from PEER to match target intensity

SP

M

Implemented

R2D UM 3.1.4.3

EL.1.3

Use GIS-Specified Matrix of Recorded Motions

SP

M

Implemented

R2D UM 2.3

EL.2

Select from Multiple Local Scale Earthquake Hazard Options

_

_

_

_

EL.2.1

Interact with PEER NGA

SP

M

Implemented

_

EL.2.1.1

Select using default selection options

SP

D

Implemented

_

EL.2.1.2

Select using all options available at PEER site

UF

D

Implemented

_

EL.2.1.3

Select using user-supplied spectrum

UF

D

Implemented

_

EL.2.2

Ability to select utilizing PEER NGA_West web service

SP

D

Implemented

R2D UM 3.1.6

EL.2.3

Ability to select from a list of user-supplied PEER motions

SP

M

Implemented

_

EL.2.4

Ability to select from a list of SimCenter motions

SP

M

Implemented

_

EL.2.5

Ability to use OpenSHA and selection methods to generate motions

UF

D

Implemented

R2D UM 3.1.2

EL.2.6

Ability to Utilize Own Application in Workflow

SP

M

EL.2.7

Ability to include Soil-Structure Interaction Effects

_

_

_

_

EL.2.7.1

1D nonlinear site response with effective stress analysis

SP

M

Implemented

R2D UM 2.3.5

EL.2.7.2

Nonlinear site response with bidirectional loading

SP

M

Implemented

_

EL.2.7.3

Nonlinear site response with full stochastic characterization of soil layers

SP

M

Implemented

_

EL.2.7.4

Nonlinear site response bidirectional different input motions

SP

M

_

EL.2.8

Ability to generate synthetic ground motions

_

_

_

_

EL.2.8.1

per Vlachos Papakonstantinou Deodatis (2017)

SP

D

Implemented

_

EL.2.8.2

per Dabaghi Der Kiureghian (2017)

UF

D

Implemented

_

EL.2.9

Ability to select from synthetic ground motions

SP

M

Implemented

_

EL.2.10

Ability to select surrogate modeling events

SP

M

Implemented

_

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

Wind Loading Requirements

Requirements - WL

#

Description

Source

Priority

Status

Implementation

WL.1

Regional Loading due to Wind Hazards

_

_

_

WL.1.1

Regional Hurricane Wind Options

_

_

_

_

WL.1.1.1

Utilize GIS and online to account for wind speed given local terrain, topography and nearby buildings

GC

D

_

_

WL.1.1.2

Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow

SP

D

InProgress

_

WL.1.1.3

Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow

SP

D

InProgress

_

WL.1.2

Modeling and simulation for determination of wind loads due to non-synoptic winds, including tornadoes

GC

D

_

_

WL.1.3

Interface with NOAA

SP

D

Implemented

_

WL.2

Local Scale Wind Hazard Options

SP

M

Implemented

_

WL.2.1

Utilize Extensive wind tunnel datasets in industry and academia for wide range of building shapes

_

_

_

_

WL.2.1.1

Accommodate Range of Low Rise building shapes

_

_

_

_

WL.2.1.1.1

Flat Shaped Roof - TPU dataset

SP

M

Implemented

_

WL.2.1.1.2

Gable Shaped Roof - TPU dataset

SP

M

Implemented

_

WL.2.1.1.3

Hipped Shaped Roof - TPU dataset

SP

M

Implemented

_

WL.2.1.2

Accommodate Range of High Rise building

SP

M

InProgress

_

WL.2.1.3

Non Isolated Low Rise Buildings - TPU dataset

SP

M

InProgress

_

WL.2.2

Interface with data-driven Interface with Vortex Winds DEDM-HRP Web service

SP

M

Implemented

_

WL.2.3

Accommodate Data from Wind Tunnel Experiment

SP

M

Implemented

_

WL.2.4

Simple CFD model generation and turbulence modeling

GC

M

Implemented

_

WL.2.5

Computational Fluid Dynamics tool for utilizing open source CFD software for practitioners

_

_

_

_

WL.2.5.1

Uncoupled OpenFOAM CFD model with nonlinear FEM code for building response

SP

M

Implemented

_

WL.2.5.2

Coupled OpenFOAM CFD model with linear FEM code for building response

SP

M

InProgress

_

WL.2.5.3

Inflow Conditions for non-synoptic winds

GC

M

_

_

WL.2.6

Quantification of Effects of Wind Borne Debris

GC

D

_

_

WL.2.7

Ability to utilize synthetic wind loading algorithms per Wittig and Sinha

SP

D

Implemented

_

WL.2.8

Hazard modification by terrain, topography, and nearby buildings

GC

D

_

_

WL.2.9

Probabilistic methods are needed to enable site-specific and storm-type specific characterization of likely debris types, weights,and speeds

GC

D

_

_

WL.2.10

Joint description for hurricane wind, storm surge, and wave hazards

GC

D

_

_

WL.2.11

Libraries of high-resolution hurricane wind/surge/wave simulations

GC

M

InProgress

_

WL.2.12

Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow

SP

M

InProgress

_

WL.2.13

Ability to select surrogate modeling events

SP

M

_

_

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

Surge/Tsunami Loading Requirements

Requirements - HL

#

Description

Source

Priority

Status

Implementation

HL.1

Regional Loading due to Storm Surge/Tsunami Hazards

GC

D

InProgress

_

HL.1.1

Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow

SP

D

InProgress

_

HL.2

Local Scale Storm Surge/Tsunami Hazard Options

GC

M

InProgress

_

HL.2.1

Using computational fluid dynamics to model the interface and loading between waves and buildings

GC

M

InProgress

_

HL.2.1.1

CFD to model fluid flow around a single rigid structure

SP

M

Implemented

_

HL.2.1.2

Mesh refinement around structures

SP

M

Implemented

_

HL.2.1.3

CFD to model fluid flow around a single deformable structure

SP

M

Implemented

_

HL.2.1.4

CFD to model fluid flow considering inflow and accumulation of fluid inside a rigid structure

SP

M

InProgress

_

HL.2.1.5

CFD to model fluid flow considering inflow, accumulation, and possible outflow of fluid across a deformable structure

SP

M

Implemented

_

HL.2.1.6

CFD to model fluid flow considering a collapsing structure

SP

M

InProgress

_

HL.2.2

Quantification of flood-borne debris hazards

GC

M

InProgress

_

HL.2.2.1

Ability to quantify the effect of unconstrained and non-colliding floating bodies

SP

M

Implemented

_

HL.2.2.2

Ability to quantify the effect of colliding flood-borne debris

SP

M

Implemented

_

HL.2.2.3

Explore multiple methods like Material Point Method (MPM), Immersed Boundary Method (IBM), DEM-CFD, Smoothed Particle Hydrodyanmics (SPH), particle tracking

SP

M

InProgress

_

HL.2.2.4

Integrate one of the methods for integrating particles with Hydro workflow

GC

M

Implemented

_

HL.2.3

Load combinations need to be developed to account for the simultaneous impacts of various flood forces, such as those generated by breaking waves, moving water and flood-borne debris

GC

M

InProgress

_

HL.2.5

Multi-scale models for wind and water flows, i.e., lower fidelity regional models with more refined models to capture local flow

SP

D

InProgress

HL.2.5.1

Interface GeoClaw and OpenFOAM

SP

M

Implemented

_

HL.2.5.2

Interface AdCirc and OpenFOAM

SP

M

InProgress

_

HL.2.6

Libraries of high-resolution hurricane wind/surge/wave simulations

SP

M

InProgress

_

HL.2.6.1

Develop a simulation library of GeoClaw simulations

SP

M

_

_

HL.2.6.2

Develop a simulation library of AdCirc simulations

SP

M

_

_

HL.2.6.3

Develop a simulation library of OpenFOAM simulations

SP

M

InProgress

_

HL.2.6.4

Develop a simulation library of MPM or SPH simulations

SP

D

InProgress

_

HL.2.6.5

Develop a simulation library of Celeris simulations

SP

D

InProgress

_

HL.2.7

Ability to simulate with surrogate models as an alternative to full 3D simulations

SP

M

InProgress

_

HL.2.7.1

Ability to simulate with surrogate models as an alternative to full 3D debris

SP

M

InProgress

_

HL.2.7.2

Ability to simulate with surrogate models as an alternative to full 3D fluids

SP

M

InProgress

_

HL.2.8

Develop digital twin(s) for wave tank facility(ies)

SP

M

Implemented

_

HL.2.8.1

Develop digital twin of the Oregon State University’s Large Wave Flume (OSU LWF) wave tank facility

SP

M

Implemented

_

HL.2.8.2

Develop digital twin of the Oregon State University’s Directional Wave Basing (OSU DWB) wave tank facility

SP

D

InProgress

_

HL.2.8.3

Develop digital twin of the Waseda University’s Tsunami Wave Basin (WU TWB) wave tank facility

SP

D

Implemented

_

HL.2.8.4

Develop digital twin of the University of Washington’s Wind-Air-Sea Interaction Facility (UW WASIRF) wave tank facility

SP

D

InProgress

_

HL.2.8.5

Develop digital twin of the Hannover Large Wave Flume (HLWF) wave tank facility

SP

D

InProgress

_

HL.2.9

Ability to utilize synthetic wave loading algorithms

SP

D

_

_

HL.2.9.1

Implement a FEMA or ASCE wave loading algorithm

SP

D

_

_

HL.2.9.2

Implement one or more cutting-edge research-based wave loading algorithms

SP

D

_

_

HL.2.9.3

Add functionality for users to create and implement their own wave loading algorithms, e.g. in Python

SP

D

_

_

HL.2.10

Ability to utilize synthetic debris loading algorithms

SP

D

_

_

HL.2.10.1

Implement a FEMA or ASCE debris loading algorithm

SP

D

_

_

HL.2.10.2

Implement one or more cutting-edge research-based debris loading algorithms

SP

D

_

_

HL.2.10.3

Add functionality for users to create and implement their own debris loading algorithms, e.g. in Python

SP

D

_

_

HL.2.11

Library for surge/tsunami debris materials, geometries, mass, mobilized speeds, likelihood of mobilization, longitudinal displacements, lateral spreading angles, uncertainties (e.g. in material properties), and correlations between parameters

SP

D

_

_

HL.2.11.1

Library for broad regions (e.g. PNW, SoCal, Hawaii, U.S. Territories, Great Lakes, Gulf-Coast, Florida, Mid-Atlantic)

SP

D

_

_

HL.2.11.2

Library for (e.g. rural, residential, industrial)

SP

D

_

_

HL.2.11.3

Library for seminal events (e.g. Tohoku 2011)

SP

D

_

_

HL.2.11.4

Library for likely future events (e.g. Cascadia M9.0 Event, Alaskan Landslide Induced Tsunami)

SP

D

_

_

HL.2.12

Probabilistic methods to enable site-specific and wave-property specific characterization of likely relevant debris types, weights, and speeds

GC

D

_

_

HL.2.12.1

Develop a probabilistic framework to quantify the likelihood of debris generation and mobilization in a given event at a given site (e.g. refine the 0.9 m flow-depth threshold)

SP

D

_

_

HL.2.12.2

Develop a probabilistic framework to couple debris mobilization, generation, and transport (e.g. random-walk / normal distribution skewed by friction anisotropy / flood vector / wind vector) with tsunami / storm-surge / wind models

SP

D

_

_

HL.2.13

Develop a PBE-compatible characterization of debris-fields (e.g. material laws, porosity, angularity)

SP

D

_

_

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

UQ Requirements

Requirements - Uncertainty Quantification Methods and Variables

#

Description

Source

Priority

Status

Implementation

UF.1

Ability to use basic Monte Carlo and LHS methods

SP

M

Implemented

r2d

UF.2

Ability to use Gaussian Process Regression

SP

M

Implemented

NA

UF.3

Ability to use Multi-Scale Monte Carlo

SP

M

_

_

UF.4

Ability to use Multi-Fidelity Models

SP

M

Implemented

NA

UF.5

Ability to use Multi-model Forward Propagation

UF

D

Implemented

NA

UR.1

Ability to use First Order Reliability method

SP

M

Implemented

NA

UR.2

Ability to use Second Order Reliability method

SP

M

Implemented

NA

UR.3

Ability to use Surrogate Based Reliability

SP

M

Implemented

NA

UR.4

Ability to use Importance Sampling

SP

M

Implemented

NA

UG.1

Ability to obtain Global Sensitivity Sobol indices

UF

M

Implemented

NA

UG.2

Ability to use probability model-based global sensitivity analysis (PM-GSA)

SP

M

Implemented

NA

UG.3

Ability to use probability model-based global sensitivity analysis (PM-GSA) for high-dimensional outputs

UF

D

Implemented

NA

US.1

Ability to Construct Gaussian Process (GP) Regression Model from a Simulation Model

SP

M

Implemented

NA

US.2

Ability to Construct GP Regression Model from Input-output Dataset

SP

M

Implemented

NA

US.3

Ability to use Surrogate Model for UQ Analysis

SP

M

Implemented

NA

US.4

Ability to Save the Surrogate Model

SP

M

Implemented

NA

US.5

Ability to Use Adaptive Design of Experiments

SP

M

Implemented

NA

US.6

Ability to Assess Reliability of Surrogate Model

SP

M

Implemented

NA

US.7

Ability to Build Surrogate Under Stochastic Excitation

SP

M

Implemented

NA

US.8

Ability to Use Physics-Informed Machine Learning

SP

M

_

_

UN.1

Ability to use Gauss-Newton solvers for parameter estimation

SP

M

Implemented

NA

UN.2

Ability to read calibration data from a file

UF

M

Implemented

NA

UN.3

Ability to handle non-scalar response quantities

UF

M

Implemented

NA

UN.4

Ability to run gradient-free parameter estimation

UF

D

Implemented

NA

UB.1

Ability to use DREAM algorithm for Bayesian inference

SP

M

Implemented

NA

UB.2

Ability to use TMCMC algorithm for Bayesian inference

SP

M

Implemented

NA

UB.3

Ability to read calibration data from a file

UF

M

Implemented

NA

UB.4

Ability to handle non-scalar response quantities

UF

M

Implemented

NA

UB.5

Ability to calibrate multipliers on error covariance

UF

M

Implemented

NA

UB.6

Ability to use a default log-likelihood function

UF

M

Implemented

NA

UB.7

Ability to use Kalman Filtering

UF

M

_

_

UB.8

Ability to use Particle Filtering

UF

M

_

_

UB.9

Ability to perform model-class selection/averaging

UF

D

Implemented

NA

UB.10

Ability to perform hierarchical Bayesian calibration

UF

D

Implemented

NA

UB.11

Ability to perform surrogate-aided Bayesian calibration

UF

D

In Progress

NA

UH.1

Ability to sample from manifold

SP

M

Implemented

NA

UH.2

Ability to build Reduced Order Model

SP

M

In Progress

NA

UO.1

Ability to use User-Specified External UQ Engine

SP

M

Implemented

NA

UO.2

Ability to use Own External FEM Application

UF

M

Implemented

NA

UO.3

Ability to use UQ Engines other than SimCenterUQ, Dakota, or UCSD-UQ

UF

P

_

_

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

RV Requirements

Requirements - Random Variables

#

Description

Source

Priority

Status

Implementation

RV.1

Various Random Variable Probability Distributions

RV.1.1

Normal

SP

M

Implemented

UM

RV.1.2

Lognormal

SP

M

Implemented

UM

RV.1.3

Uniform

SP

M

Implemented

UM

RV.1.4

Beta

SP

M

Implemented

UM

RV.1.5

Weibull

SP

M

Implemented

UM

RV.1.6

Gumbel

SP

M

Implemented

UM

RV.1.7

Exponential

SP

M

Implemented

_

RV.1.8

Discrete

SP

M

Implemented

_

RV.1.9

Gamma

SP

M

Implemented

_

RV.1.10

Chi-squared

SP

M

Implemented

_

RV.1.11

Truncated Exponential

SP

M

Implemented

_

RV.2

User-defined Distribution

SP

M

_

_

RV.3

Define Correlation Matrix

SP

M

Implemented

UM

RV.4

Random Fields

SP

M

_

_

RV.5

Ability to View Graphically the density function when defining the RV

UF

D

Implemented

UM

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code

Common Research Application Requirements

Requirements - CR

#

Description

Source

Priority

Status

Implementation

CR.1

Open-source software where developers can test new data and develop algorithms

CR.1.1

Provide open-source applications utilizing code hosting platforms, e.g. GitHub

SP

M

Implemented

R2D

CR.1.2

Assign an open-source license that allows free use

SP

M

Implemented

R2D

CR.2

Ability to use multiple coupled resources (applications, databases, viz tools) by Practicing Engineers

CR.2.1

Allow users to launch scientific workflows

SP

M

Implemented

R2D

CR.3

Ability to utilize resources beyond the desktop including HPC

CR.3.1

Allow users to utilize HPC resources at TACC through DesignSafe

SP

M

Implemented

R2D

CR.4

Efficient use of multiple coupled and linked models requiring sharing and inter-operability of databases, computing environments, networks, visualization tools, and analysis systems

CR.4.1

Identify and include external analysis systems

SP

M

InProgress

_

CR.4.2

Identify and include external databases

SP

M

InProgress

UM

CR.4.3

Identify and include external viz tools

SP

M

InProgress

SC

CR.4.4

Identify and include external computing env

SP

M

Inprogress

1.1.2.5.5

CR.5

Tool available for download from web

CR.5.1

Tool downloadable from DesignSafe website

GC

M

Implemented

R2D

CR.6

Ability to benefit from programs that move research results into practice and obtain training

CR.6.1

Ability to use educational provisions to gain interdisciplinary education for expertise in earth sciences and physics, engineering mechanics, geotechnical engineering, and structural engineering to be qualified to perform these simulations

GC

D

_

_

CR.6.2

Documentation exists demonstrating application usage

SP

M

Implemented

_

CR.6.3

Video exists demonstrating application usage

SP

M

Implemented

_

CR.6.4

Tool training through online and in-person training events

SP

M

Implemented

_

CR.7

Verification examples exist

SP

M

Implemented

R2D

CR.8

Validation of proposed analytical models against existing empirical datasets

CR.8.1

Validation examples exist, validated against tests or other software

GC

M

_

CR.9

Tool to allow users to load and save user inputs

SP

M

Implemented

core

CR.10

Installer which installs the application and all needed software

UF

D

Implemented

R2D

Key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implemented, InProgress, and Blank (i.e. not started)
Implementation: UM=User Manual, DM=Developer Manual, SC=Source Code