5. 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.

Table 5.1 Requirements - R2D

#

Description

Source

Priority

Version

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

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.1.2

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

GC

M

R2D.1.3

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

GC

M

InProgress

R2D.1.4

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

GC

M

R2D.1.5

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

GC

M

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

R2D.1.9

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

SP

M

R2D.2

Include Different Asset Types

GC

M

InProgress

R2D.2.1

Ability to incorporate building assets

GC

M

Implemented

R2D.2.1.1

Ability to incorporate multi-fidelity building model asset descriptions

GC

M

R2D.2.2

Ability to incorporate transportation networks

GC

M

R2D.2.3

Ability to incorporate utility networks

GC

M

R2D.2.3.1

Methods to overcome national security issues with certain utility data

GC

M

R2D.2.4

Ability to incorporate surrogate models in asset modeling

SP

M

R2D.3

Include Different Analysis options

GC

M

InProgress

R2D.3.1

Ability to include multi-scale nonlinear models

GC

M

Implemented

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.4.2

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

SP

M

Implemented

R2D.4.3

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

SP

M

R2D.4.4

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

SP

M

R2D.4.5

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

SP

M

R2D.4.6

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

SP

M

R2D.4.7

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

SP

M

R2D.4.8

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

SP

M

R2D.4.9

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

SP

M

R2D.4.10

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

SP

M

R2D.4.11

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

SP

M

R2D.5

Include Different Response/Recovery options

GC

M

R2D.5.1

Response/Recovery options for households

SP

M

R2D.5.2

Response/Recovery options for infrastructure

SP

M

R2D.5.3

Response/Recovery options for business operations

SP

M

R2D.5.4

Response/Recovery and Effect on Environment

SP

M

R2D.5.4.1

CO2 emissions from demolition and repair

SP

M

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

R2D.6.3

Ability to capture uncertainty of results in visualization

SP

P

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.7.2

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

GC

M

Implemented

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.7.4

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

GC

M

Implemented

R2D.7.5

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

GC

M

R2D.7.7

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

GC

P

R2D.7.8

Ability to use system that creates and monitors real-time data, updates models, incorporates crowdsourcing 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

R2D.7.11

Incorporate programs that can address lifeline network disruptions and network interdependencies

GC

M

R2D.7.12

Application to Provide Common SimCenter Research Application Requirements listed in CR (not already listed above)

GC

M

InProgresss

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)

5.1. Earthquake Loading Requirements

Table 5.1.1 Requirements - EL

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

EL.1.1.3

Interface between asset and regional simulations using site response method

SP

M

EL.1.1.4

Interface between asset and regional simulations using DRM method

SP

M

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 grouind motions from PEER to match target intensity

SP

M

Implemented

EL.1.3

Use GIS-Specified Matrix of Recorded Motions

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

EL.2.3

Ability to select from list of user supplied PEER motions

SP

M

Implemented

EL.2.4

Ability to select from list of SimCenter motions

SP

M

Implemented

EL.2.5

Ability to use OpenSHA and selection methods to generate motions

UF

D

EL.2.6

Ability to Utilize Own Application in Workflow

SP

M

Implemented

EL.2.7.1

1D nonlinear site response with effective stress analysis

SP

M

Implemented

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.2.8.1

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

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)

5.2. Wind Loading Requirements

Table 5.2.1 Requirements - WL

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

MultiScale Wind Models

SP

D

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

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

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

WL.2.1.1.3

Hipped Shaped Roof - TPU dataset

SP

M

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.2.5.1

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

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

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: Implements, InProgress and Blank (i.e. not started)

5.3. Surge/Tsunami Loading Requirements

Table 5.3.1 Requirements - HL

#

Description

Source

Priority

Status

HL

Loading from Storm Surge/Tsunami on Local and Regional Assets

HL.1

Regional Loading due to Storm Surge/Tsunami Hazards

GC

M

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

HL.2

Local Scale Storm Surge/Tsunami Hazard Options

HL.2.1

Using computational fluid dynamics to model interface and impact between water loads and buildings

GC

M

HL.2.1.1

CFD to model fluid flow around a single rigid structure

SP

M

HL.2.1.2

Mesh refinement around structures

SP

M

HL.2.1.3

CFD to model fluid flow around a single deformable structure

SP

M

HL.2.1.4

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

SP

M

HL.2.1.5

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

SP

M

HL.2.2

Quantification of flood-borne debris hazards

GC

M

HL.2.2.1

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

SP

M

HL.2.2.2

Ability to quantify the effect of colliding flood-borne debris

SSP

M

HL.2.2.3

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

SP

M

HL.2.2.4

Integrate one of the methods for integrating particles with Hydro workflow

GC

M

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

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

HL.2.5.1

Interface GeoClaw and OpenFOAM

SP

M

HL.2.5.2

Interface AdCirc and OpenFOAM

SP

M

HL.2.6

Libraries of high resolution hurricane wind/surge/wave simulations

GC

M

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

HL.2.7

Ability to simulate with surrogate models as alternative to full 3D CFD

SP

M

HL.2.8

Develop digital twin with OSU wave Tank Facility

SP

M

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)

5.4. UQ Requirements

Table 5.4.1 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

UF.2

Ability to use Gaussian Process Regression

SP

M

Implemented

UF.3

Ability to use Own External UQ Engine

SP

M

UF.4

Ability to use Multi-Scale Monte Carlo

SP

M

NA

UF.5

Ability to use Multi-Fidelity Models

SP

M

NA

UR.1

Ability to use First Order Reliability method

SP

M

Implemented

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

UG.2

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

SP

M

Implemented

US.1

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

SP

M

InProgress

US.2

Ability to Construct GP Regression Model from Input-output Dataset

SP

M

InProgress

US.3

Ability to use Surrogate Model for UQ Analysis

SP

M

InProgress

US.4

Ability to Save the Surrogate Model

SP

M

InProgress

US.5

Ability to Use Adaptive Design of Experiments

SP

M

InProgress

US.6

Ability to Asses Reliability of Surrogate Model

SP

M

InProgress

US.7

Ability to Build Surrogate Under Stochastic Excitation

SP

M

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 file

UF

M

InProgress

NA

UN.3

Ability to handle non-scalar response quantities

UF

M

InProgress

NA

UB.1

Ability to use DREAM algorithm for Bayesian inference

SP

M

InProgress

NA

UB.2

Ability to use TMCMC algorithm for Bayesian inference

SP

M

Implemented

NA

UB.3

Ability to read calibration data from file

UF

M

InProgress

NA

UB.4

Ability to handle non-scalar response quantities

UF

M

InProgress

NA

UB.5

Ability to calibrate multipliers on error covariance

UF

M

NA

UB.6

Ability to use a default log-likelihood function

UF

M

NA

UB.7

Ability to use Kalman Filtering

UF

M

NA

UB.8

Ability to use Particle Filtering

UF

M

NA

UH.1

Ability to sample from manifold

SP

M

NA

UH.2

Ability to build Reduced Order Model

SP

M

NA

UO.1

Ability to use User-Specified External UQ Engine

SP

M

Implemented

UO.2

Ability to use Own External FEM Application

UF

M

Implemented

UM.1

Ability to use various Reliability Methods

-

-

-

-

UM.1.1

Ability to use First Order Reliability method

UF

M

2.1

NA

UM.1.2

Ability to use Surrogate Based Reliability

UF

M

NA

UM.1.3

Ability to use Own External Application to generate Results

UF

M

2.2

NA

UM.2

Ability to user various Sensitivity Methods

-

-

-

-

UM.2.1

Ability to obtain Global Sensitivity Sobol’s indices

UF

M

NA

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)

5.5. RV Requirements

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)

5.6. Common Research Application Requirements

Table 5.6.1 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

link

CR.1.2

Assign an open-source licensce that allows free use.

SP

M

Implemented

link

CR.2

Ability of Practicing Engineers to use multiple coupled resources (applications, databases, viz tools) in engineering practice

-

-

-

-

CR.2.1

Allow users to launch scientific workflows

SP

M

Implemented

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

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

CR.4.3

Identify and include external viz tools

SP

M

InProgress

CR.4.4

Identify and include external computing env

SP

M

Inprogress

CR.5

Tool available for download from web

-

-

-

-

CR.5.1

Tool downloadable from DesignSafe website

GC

M

Implemented

link

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 interdisclipinary education so as to gain expertise in earth sciences and physics, engineering mechanics, geotechnical engineering, and structural engineering in order to be qualified to perform these simulations

GC

D

CR.6.2

Documentation exists demonstrainting 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

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

link

CR.9

Tool to allow user to load and save user inputs

SP

M

Implemented

core

CR.10

Installer which installs application and all needed software

UF

D

link

key:
Source: GC=Needed for Grand Challenges, SP=Senior Personnel, UF=User Feedback
Priority: M=Mandatory, D=Desirable, P=Possible Future
Status: Implements, InProgress and Blank (i.e. not started)