# Executive Summary

# December 2001

**Vision:**

The development and maintenance of specialized monolithic reacting flow
modeling codes is increasingly challenging in the limit of massively
parallel computing. Similarly, the analysis and understanding of
associated databases is a serious challenge given the complexity of
multidimensional transient chemistry-transport coupling addressed in
these codes. This project aims to advance the state of the art in both
these areas by pursuing the development of a flexible reacting flow
computation and analysis toolkit based on the Common Component
Architecture (CCA). This toolkit will utilize high-order Adaptive Mesh
Refinement (AMR) for reacting flow computations, and will use the
Computational Singular Perturbation (CSP) technique for analysis of
reacting flow. By focusing on the development of components, not codes,
within the CCA framework, we will arrive at a suite of flexible and
reusable reacting flow tools whose utilization will reduce the cost of
developing and maintaining reacting flow science codes. This toolkit
will be used to assemble different reacting flow science codes and to
use them for massively parallel reacting flow modeling and analysis
studies. These developments will :

-   *Enable massively parallel large-scale reacting flow modeling
    studies, allowing investigation of 3D turbulence-chemistry
    interaction with detailed kinetics, as well as a host of other
    large-scale reacting flow problems.*

-   *Enable the extraction of physical understanding from large-scale
    reacting flow databases, identifying cause-and-effect relationships
    and allowing automatic chemical model reduction.*

**Major Goals and Technical Challenges:**

This project has two broad goals:

1.  We will implement and demonstrate a new approach and architecture
    for the construction and utilization of massively parallel software
    for scientific research in reacting flow modeling. We will migrate
    existing algorithms and codes, and implement new ones where
    necessary, into a toolkit for reacting flow modeling based on the
    Common Component Architecture (CCA). This will involve segregation
    of functionalities typically interconnected in existing
    high-performance massively-parallel scientific computing codes into
    distinct reusable components. This approach is driven by the need to
    reduce the challenges in developing and maintaining advanced
    massively parallel *codes*, by shifting the focus towards
    development and maintenance of flexible and reusable *peer
    components*. These can be developed by experts in the relevant areas
    with little interdependence except as regards the definitions of
    component interfaces. While this is easily and routinely done with
    commercial software, there are challenges pertaining to
    high-performance massively parallel scientific codes. There are
    clear tradeoffs between flexibility and performance as regards
    component *size*, as communication within a component is faster and
    more efficient than external communication, yet component
    flexibility may vary inversely with size. Therefore, one key
    challenge is the identification of optimal component designs as
    regards both functionality and interface definition. Other
    challenges pertain to the utilization of high-order Structured
    Adaptive Mesh Refinement (SAMR) in a massively parallel context with
    dynamic load balancing. Work is required to take existing, and new,
    algorithms/codes for SAMR reacting flow computations to a high-order
    load-balanced component-based implementation. Realization of these
    goals will result in a high-fidelity high-performance toolkit of
    reacting flow components that can be assembled into different
    reacting flow codes. We plan to demonstrate this reusability via the
    assembly and utilization of low Mach number and compressible DNS
    codes for turbulent reacting flow modeling, in collaboration with
    the Turbulent Combustion DNS SciDAC project.

2.  We will develop and demonstrate an advanced Computational Singular
    Perturbation (CSP) analysis component toolkit that enables
    extraction of enhanced physical understanding from reacting flow
    databases. These tools will allow the identification of cause and
    effect relationships, as well as automatic chemical model reduction
    in the context of transport-chemistry coupling in multidimensional
    reacting flow. Massively parallel computing has led to the
    generation of large-scale reacting flow databases involving complex
    multidimensional coupling between transient chemical and transport
    processes. The extraction of physical understanding in these coupled
    systems is a major challenge. CSP tools are an essential ingredient
    in the analysis of physical processes implicit, but not
    self-evident, in these computational results. We will also use CSP
    to identify local equilibrium manifolds, enabling automatic chemical
    reduction. This information is a prerequisite for potential future
    adaptive chemistry constructions. Key challenges towards the
    attainment of the goals in this area include the development of a
    robust and accurate formulation for transport-chemistry coupling in
    CSP when transport and chemical time scales are comparable, the use
    of efficient means for accessing and mining large data sets, and the
    parallel implementation and adaptive tabulation of CSP analysis
    including eigenvalue solutions.

**Major Milestones and Activities:**

-   Year 1:

    -   Develop chemistry, thermodynamic, and transport property
        components.

    -   Develop operator-split and stiff time integration components.

    -   Develop 2D high-order spatial discretization components.

    -   Demonstrate 2D reaction-diffusion components with GrACE/AMR.

-   Year 2:

    -   Develop projection/pressure components and coupled
        species/momentum integration.

    -   Demonstrate CSP analysis components.

    -   Demonstrate component interchangeability working with the SciDAC
        DNS project.

    -   Demonstrate 2D low Mach number reacting flow component assembly
        and computation.

-   Year 3:

    -   Complete testing and optimization of assembled components into a
        GrACE/AMR code.

    -   Demonstrate and optimize CSP automatic chemical
        simplification/reduction components.

    -   Evaluate efficiency and flexibility tradeoffs in the utilization
        of the component toolkit for reacting flow computations.

    -   Demonstrate component-based, balanced, scalable, parallel 3D low
        Mach number reacting flow GrACE/AMR computations.

**Current/Potential Connections with Other SciDAC Projects:**

-   \"Center for Component Technology for Terascale Simulation
    Software\", led by R. Armstrong of Sandia. This project is a key
    partner in the design and implementation of components. (current)

-   \"Terascale High-Fidelity Simulations of Turbulent Combustion with
    Detailed Chemistry\", led by A. Trouve of U. Maryland. This project
    is a key partner in the definition and optimization of component
    interfaces, and for the demonstration of component flexibility.
    (current)

-   \"The Terascale Simulation Tools and Technologies (TSTT) Center\",
    led by J. Glimm of Brookhaven. This project is a resource for
    high-order numerical discretization on structured adaptive meshes.
    (current)

-   "The Collaboratory for Multi-Scale Chemical Science (CMCS)", led
    by L. Rahn of Sandia. This project is a resource in facilitating
    document and information sharing on our project. It will also be a
    partner in the general area of data analysis, feature tracking, and
    visualization. (current)

-   \"Scientific Data Management (SDM)\" led by A. Shoshani of LBNL. We
    have existing connections with this project in the area of
    management and mining of very large-scale data sets. (current)

-   "Terascale Optimal PDE Solvers (TOPS)" led by D. Keyes of Old
    Dominion University. We see potential connections with this project
    in the area of eigenanalysis and adaptive time integration.
    (potential)