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 Requirement Traceability Matrix (RTM) specific to the Optimization module.

Introduction

Minimum System Requirements

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

  • GCC/Clang C++17 compliant compiler (GCC @ 7.5.0, Clang @ 10.0.1 or greater)

    • Note: Intel compilers are not supported.

  • Memory: 8 GBs of RAM for optimized compilation (16 GBs for debug compilation), 2 GB per core execution

  • Processor: 64-bit x86 or ARM64 (specifically, Apple Silicon)

  • Disk: 30GB

  • A Portable Operating System Interface (POSIX) compliant Unix-like operating system, including the two most recent versions of MacOS and most current versions of Linux.

  • Git version control system

  • Python @ 3.7 or greater

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.

System Requirements Traceability

Functional Requirements

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.