6. 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.

6.1. General Requirements

Table 6.1.1 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

6.2. Earthquake Loading Requirements

Table 6.2.1 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

6.3. Wind Loading Requirements

Table 6.3.1 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

6.4. Surge/Tsunami Loading Requirements

Table 6.4.1 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

6.5. UQ Requirements

Table 6.5.1 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

6.6. RV Requirements

Table 6.6.1 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

6.7. Common Research Application Requirements

Table 6.7.1 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