7. Requirements

The following page contains the functional requirements for the HydroUQ application. These requirements are broken down into a number of groups, general, surge/tsunami loading, building description, analysis, UQ, RV, and CR.

The purpose of presenting these requirements is to inform the community about the present capabilities of the HydroUQ application and features that could be added. The original set of requirements has come from a set of grand challenge reports, GC. These original requirements have been broken into a smaller set of deliverable features by the senior faculty associated with the project, SP. Additional requirements have come from users, U. Go to the Bugs & Feature Requests section if there are additional features you would like to see added.

7.1. General Requirements

The following are the requirements for the response of a single structure due to wave or hydrodynamic loading effects of water caused by a tsunami or coastal inundation during a hurricane. The requirements are being met by the Hydro-UQ application. All requirements in this section are related to work in WBS 1.3.7.

Table 7.1.1 Requirements - General

#

Description

Source

Priority

Status

Implementation

H

Application to determine response of a Building Subject to Water Action due to Storm Surge or Tsunami Hazards including formal treatment of randomness and uncertainty

GC

M

InProgress

Application

H.1

Quantification of water-borne debris hazards

GC

M

InProgress

1.1.2.1.2

H.1.1

Multiple debris impacts in a solitary-like wave

GC

M

Implemented

Example

H.1.2

Multiple debris impacts in a tsunami-like wave

GC

M

Implemented

Example

H.1.3

Multiple debris impacts in a surge-like wave

GC

M

InProgress

1.1.2.1.2

H.2

Effects of over-land flow, including waves, debris, flood velocity, wind-driven influences, erosion effects at buildings and channeling effects of the built environment

H.2.1

Effects of over-land flow

GC

D

Implemented

Example

H.2.2

Effects of waves, e.g. solitary vs irregular type

GC

D

Implemented

Example & Example

H.2.3

Effects of debris

GC

D

Implemented

Example

H.2.4

Effects of flood velocity

GC

D

Implemented

Example

H.2.5

Effects of wind-driven influences

GC

D

H.2.6

Effects of erosion at buildings

GC

D

H.2.7

Effects of flow channeling in the built environment

GC

D

Implemented

Example

H.2.8

Effects of frictional resistance with respect to debris hazards

SP

D

Implemented

hdro-0004

H.3

Ability to select from all Loading Options listed in HL.2

H.3.1

Ability to define local-scale tsunami loading events

SP

M

InProgress

1.1.2.1.2

H.3.2

Ability to define local-scale surge loading events

SP

M

InProgress

1.1.2.1.2

H.4

Ability to select from Building Modeling Options listed in MOD under BM

SP

M

Implemented

SIM

H.5

Include ability to perform nonlinear analysis on the building models listed in ANA

SP

M

Implemented

FEM

H.5.1

Implement ability to perform a strongly two-way coupled analysis between the fluid and structural domains

SP

M

Implemented

hdro-0003

H.5.2

Implement ability to perform a unified analysis between the fluid and structural domains

SP

M

Implemented

hdro-0002

H.5.3

Implement ability to perform a strongly two-way or unified analysis between the debris-fluid-structure domains

SP

M

Implemented

hdro-0002

H.6

Ability to use Various UQ Methods and Variable Options

H.6.1

Ability to use Forward Propagtion methods listed in UQ under UF

SP

M

InProgress

UQ

H.6.2

Ability to use Random Variable Distributions defined in RV

SP

M

Implemented

RV

H.6.3

Ability to use Reliability Methods listed in UQ under UR

SP

M

InProgress

H.6.4

Ability to use Global Sensitivity Methods listed in UQ under UG

SP

M

InProgress

H.6.5

Ability to both use and create surrogates listed in UQ under US

SP

M

InProgress

1.1.2.2.2 & 1.1.2.2.3

H.6.6

Ability to use High Dimensional UQ listed in UQ under UH

SP

M

H.7

Ability to Visualize the Results

H.7.1

Ability to view individual sample results

SP

M

Implemented

Example

H.7.2

Ability to graphically view the results to show distributions in structural responses

SP

M

Implemented

RES

H.8

Miscelleneous User Requests

H.8.1

Ability to quickly model experimental tests performed in Oregon State University’s Large Wave Flume (OSU LWF) wave tank

UF

M

Implemented

Example

H.8.1

Ability to quickly model experimental tests performed in Waseda University’s Tsunami Wave Basin (WU TWB) wave tank

UF

D

Implemented

Example

H.9

General Software Requirements

H.9.1

Application to Provide Common SimCenter Research Application Requirements listed in CR

GC

M

InProgress

1.1.2.5.1

H.10

Tool should incorporate data from world-wide-web (www)

H.10.1

Tool should use satelite imagery in aid of determine channeling effect

SP

D

H.10.2

Tool should use satelite imagery in aid of determining amount of debris

SP

D

InProgress

1.1.3.5.2

H.10.3

Tool should obtain building modelling info from database through www

SP

D

H.10.4

Tool should obtain bathymetry elevation data through www

UF

D

InProgress

NOAA Digital Coast

H.10.5

Tool should obtain sea-level rise data through www

UF

D

InProgress

NOAA Digital Coast

H.10.6

Tool should incorporate hardware-accelerated web-applications through www

UF

D

InProgress

Celeris-WebGPU

H.11

Library for surge/tsunami debris materials, geometries, longitudinal andlateral spread, uncertainties and correlations between parameters

SP

D

Proposed

Mar 2025

H.11.1

Library for broad regions, e.g. PNW, SoCal, Hawaii, or the Great Lakes

SP

D

Proposed

Mar 2025

H.11.2

Library for land-use classifications, e.g. rural, residential, industrial

SP

D

Proposed

Mar 2025

H.11.3

Library for seminal events, e.g. Tohoku 2011

SP

D

Proposed

Mar 2025

H.11.4

Library for a likely future event, e.g. Cascadia M9.0 Event

SP

D

Proposed

Mar 2025

H.12

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

GC

D

Proposed

Jan 2025

H.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

Proposed

Jan 2025

H.12.2

Develop a probabilistic framework to couple debris mobilization,generation,and transport, e.g. random-walk skewed by friction anisotropy, flood and wind vectors

SP

D

Proposed

Jan 2025

H.13

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

SP

D

Proposed

Jan 2025
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

7.2. Loading Requirements

Table 7.2.1 Requirements - Storm-Surge / Tsunami Loading

#

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

1.1.3.3.2

HL.2

Local Scale Storm Surge/Tsunami Hazard Options

GC

M

InProgress

1.1.2.1.2

HL.2.1

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

GC

M

InProgress

1.1.2.1.2

HL.2.1.1

CFD to model fluid flow around a single rigid structure

SP

M

Implemented

UM

HL.2.1.2

Mesh refinement around structures

SP

M

Implemented

hdro-0003

HL.2.1.3

CFD to model fluid flow around a single deformable structure

SP

M

Implemented

hdro-0003

HL.2.1.4

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

SP

M

InProgress

1.1.2.1.2

HL.2.1.5

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

SP

M

Implemented

hdro-0003

HL.2.1.6

CFD to model fluid flow considering a collapsing structure

SP

M

InProgress

1.1.3.5.2

HL.2.2

Quantification of flood-borne debris hazards

GC

M

InProgress

hdro-0002, hdro-0004

HL.2.2.1

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

SP

M

Implemented

hdro-0002

HL.2.2.2

Ability to quantify the effect of colliding flood-borne debris

SP

M

Implemented

hdro-0004

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

hdro-0004

HL.2.2.4

Integrate one of the methods for integrating particles with Hydro workflow

GC

M

Implemented

hdro-0004

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

hdro-0002

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

1.1.3.3.2

HL.2.5.1

Interface GeoClaw and OpenFOAM

SP

M

Implemented

UM

HL.2.5.2

Interface AdCirc and OpenFOAM

SP

M

InProgress

1.1.2.1.2, 1.1.3.3.2

HL.2.6

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

SP

M

InProgress

1.1.1.1.4

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

1.1.1.1.4

HL.2.6.4

Develop a simulation library of MPM or SPH simulations

SP

D

InProgress

1.1.1.1.4

HL.2.6.5

Develop a simulation library of Celeris simulations

SP

D

InProgress

1.1.1.1.4

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

1.1.2.2.2

HL.2.7.2

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

SP

M

InProgress

1.1.2.2.3

HL.2.8

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

SP

M

Implemented

hdro-0002

HL.2.8.1

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

SP

M

Implemented

hdro-0002

HL.2.8.2

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

SP

D

InProgress

1.1.2.5.1

HL.2.8.3

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

SP

D

Implemented

hdro-0004

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

1.1.2.5.1

HL.2.8.5

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

SP

D

InProgress

1.1.2.5.1

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

7.3. Modeling Requirements

Table 7.3.1 Requirements - Modeling

#

Description

Source

Priority

Status

Implementation

MOD

Asset Model Generators for Analysis

BM

Asset Model Generators for Buildings

BM.1

Ability to quickly create a simple nonlinear building model

GC

D

Implemented

UM

BM.2

Ability to use existing OpenSees model scripts

SP

M

Implemented

hdro-0001

BM.3

Ability to define a building and use Expert System to generate FE mesh

SP

D

Implemented

_

BM.4

Ability to define a building and use Machine Learning applications to generate FE

GC

D

_

_

BM.5

Ability to specify connection details for member ends

UF

D

_

_

BM.6

Ability to define a user-defined moment-rotation response representing the connection details

UF

D

_

_

BM.7

Ability to incorporate AutoSDA Steel Design Application in Local Applications

UF

M

Implemented

_

BM.8

Ability to use user-supplied Python script to generate mesh

UF

M

InProgress

SC

BM.9

Ability to use multiple models of similar fidelity

SP

M

Implemented

BM.10

Ability to use multiple models of different fidelity

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

7.4. Analysis Requirements

Table 7.4.1 Requirements - Analysis

#

Description

Source

Priority

Status

Implementation

ANA.1

Ability to select from different Nonlinear Analysis options

_

_

_

_

ANA.1.1

Ability to specify OpenSees as FEM engine and to specify different analysis options

SP

M

Implemented

_

ANA.1.2

Ability to provide own OpenSees Analysis script to OpenSees engine

SP

D

Implemented

_

ANA.1.3

Ability to provide own Python script and use OpenSeesPy engine

SP

D

_

_

ANA.1.4

Ability to use alternative FEM engines

SP

M

_

_

ANA.2

Ability to know if an analysis run fails

UF

M

_

_

ANA.3

Ability to specify Modal Damping

_

_

_

_

ANA.3.1

Ability to specify damping ratio as a random variable

UF

M

Implemented

_

ANA.3.2

When using Rayleigh Damping, ability to specify the two modes used to calculate damping parameters

UF

M

Implemented

UM

ANA.4

Ability to run for more than 60 hours at DesignSafe

UF

D

_

_

ANA.5

Ability to specify the number of iterations in convergence test

UF

M

Implemented

UM

ANA.6

Ability to use multiple analysis options

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

7.5. UQ Requirements

Table 7.5.1 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

hdro-0001

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

UM

UF.5

Ability to use Multi-model Forward Propagation

UF

D

Implemented

UM

UR.1

Ability to use First Order Reliability method

SP

M

Implemented

UM

UR.2

Ability to use Second Order Reliability method

SP

M

Implemented

UM

UR.3

Ability to use Surrogate Based Reliability

SP

M

Implemented

UM

UR.4

Ability to use Importance Sampling

SP

M

Implemented

UM

UG.1

Ability to obtain Global Sensitivity Sobol indices

UF

M

Implemented

UM

UG.2

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

SP

M

Implemented

UM

UG.3

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

UF

D

Implemented

UM

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

7.6. RV Requirements

Table 7.6.1 Requirements - Random Variables

#

Description

Source

Priority

Status

Implementation

RV.1

Various Random Variable Probability Distributions

RV.1.1

Normal

SP

M

Implemented

hdro-0001

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

7.7. Common Research Application Requirements

Table 7.7.1 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

HydroUQ

CR.1.2

Assign an open-source license that allows free use

SP

M

Implemented

HydroUQ

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

HydroUQ

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

HydroUQ

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

1.1.2.5.5

CR.5

Tool available for download from web

CR.5.1

Tool downloadable from DesignSafe website

GC

M

Implemented

HydroUQ

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

_

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

HydroUQ
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