Optimization Requirements Traceability Matrix

This template follows INL template TEM-214, "IT System Requirements Traceability Matrix."

commentnote

This document serves as an addendum to Framework Requirements Traceability Matrix and captures information for RTM specific to the Optimization module.

Introduction

Minimum System Requirements

In general, the following is required for MOOSE-based development:

A POSIX compliant Unix-like operating system. This includes any modern Linux-based operating system (e.g., Ubuntu, Fedora, Rocky, etc.), or a Macintosh machine running either of the last two MacOS releases.

HardwareInformation
CPU Architecturex86_64, ARM (Apple Silicon)
Memory8 GB (16 GBs for debug compilation)
Disk Space30GB

LibrariesVersion / Information
GCC9.0.0 - 12.2.1
LLVM/Clang10.0.1 - 19
Intel (ICC/ICX)Not supported at this time
Python3.10 - 3.13
Python Packagespackaging pyaml jinja2

System Purpose

The purpose of the MOOSE Optimization module is to control model parameters such that they minimize a defined objective. An example would be to search for material property values so that the solution matches experimental results. As such, the purpose of this module is not to provide physical model capabilities, which is typically the responsibility of other MOOSE modules and dependent applications.

System Scope

The MOOSE Optimization module utilizes several MOOSE systems to perform optimization on physics models. The design relies on a customized Executioner to drive the optimization algorithm. This Executioner calls a specific type of Reporter known as a OptimizationReporter, which defines the parameter space, objective, gradient, and Hessian functions necessary for the optimization methods. Along with calling this Reporter, the Executioner calls MultiApps and Transfers that are responsible for passing parameters to the physics model, running the model, and gathering the necessary data to compute the objective, gradient, and Hessian. Typically, one sub-application defines the underlying physics for the objective computation, another defines an adjoint version of the model for the gradient computation, and another defines a homogeneous version of the model for the matrix-free Hessian computation. All data from these applications is gathered in the OptimizationReporter, which is then sent to the optimization Executioner. The resulting output is the value of the optimized parameters and the solution of physics model with these parameters. The scope of the Optimization module includes, but is not limited to:

  • Inverse optimization

  • Shape optimization

  • Topology optimization

Assumptions and Dependencies

The Optimization module is developed using MOOSE and can itself be based on various MOOSE modules, as such the RTM for the Optimization module is dependent upon the files listed at the beginning of this document.

Pre-test Instructions/Environment/Setup

Ideally all testing should be performed on a clean test machine following one of the supported configurations setup by the test system engineer. Testing may be performed on local workstations and cluster systems containing supported operating systems.

The repository should be clean prior to building and testing. When using "git" this can be done by doing a force clean in the main repository and each one of the submodules:

git clean -xfd
git submodule foreach 'git clean -xfd'

All tests must pass in accordance with the type of test being performed. This list can be found in the Software Test Plan.

Changelog Issue Revisions

Errors in changelog references can sometimes occur as a result of typos or conversion errors. If any need to be noted by the development team, they will be noted here.

The changelog for all code residing in the MOOSE repository is located in the MOOSE RTM.

System Requirements Traceability

Functional Requirements

  • optimization: Outputs
  • 11.6.1The system shall be able to perform gradient based material parameter inversion for a single material property and output the iteration-wise exodus output for the adjoint problem.

    Specification(s): adjoint_grad_iteration_output

    Design: ExodusOptimizationSteady

    Issue(s): #25009

    Collection(s): FUNCTIONAL

    Type(s): Exodiff

    27
  • 11.6.2The system shall be able to invert for point loads using gradient-based optimization with an automatically computed adjoint and output the exodus output per iteration for the combined forward and adjoint problem variables.

    Specification(s): auto_adjoint_iteration_output

    Design: ExodusOptimizationSteady

    Issue(s): #25009

    Collection(s): FUNCTIONAL

    Type(s): Exodiff

    27

Usability Requirements

No requirements of this type exist for this application, beyond those of its dependencies.

Performance Requirements

No requirements of this type exist for this application, beyond those of its dependencies.

System Interface Requirements

No requirements of this type exist for this application, beyond those of its dependencies.

References

No citations exist within this document.