Draft statement of work



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1.0Introduction


Offeror may replace this section in its technical proposal(s) response with an overview of the proposed Dawn and Sequoia systems, technology development, project plan and build strategy.

1.1NNSA’s Stockpile Stewardship Program and Complex 2030


The National Nuclear Security Administration (NNSA) Advanced Simulation and Computing (ASC) computational resources are essential to enable nuclear weapon scientists to fulfill stockpile stewardship requirements through simulation in lieu of underground testing. Modern simulations on powerful computing systems are key to supporting our national security mission. As the nuclear stockpile moves further from the nuclear test base through either the natural aging of today’s stockpile or introduction of modifications, the realism and accuracy of ASC simulations must further increase through development of improved physics models and methods requiring ever greater computational resources.

Problems at the highest end of this computational spectrum have been, and will continue to be, a principal driver for the ASC Program as highly predictive codes are developed (as outlined in the ASC Roadmap1 and the evolving Predictive Capability Framework2) between 2008 and 2020. Predictive simulation of nuclear weapons performance requires rigorous assessment of margins and quantification of uncertainties. To be predictive, these uncertainties must be small enough to allow the certification of nuclear warheads without resorting to underground nuclear tests. Predictive simulation eliminates the technical need for future nuclear tests.

Reducing uncertainties sufficiently for predictive simulation requires advances in the fidelity of physics models, the accuracy of numerical algorithms, and their resolution and the ability to assess uncertainty – all ASC Program Roadmap goals. These in turn are dependent on the level of computing that can be brought to bear. The ASC Program requires an appropriate mix of platforms to quantify uncertainties and to predict with confidence. Capability, together with capacity and advanced architecture, systems are components of the balanced triad necessary for success in weapons simulation, as described in the ASC Platform Plan. The ASC Platform Plan describes the need for new computing resources to support uncertainty quantification (UQ) and reduction in phenomenology (i.e., replacing calibrated models with physics-based models).

As part of the Stockpile Stewardship Program Plan3, the National Nuclear Security Administration (NNSA) Defense Programs (DP) recently set forth a goal for transforming the nuclear weapons complex into a responsive, modern infrastructure over the next two decades, while continuing to address needs in the enduring national nuclear weapons stockpile, as warheads age and move further from the test base. A modern, responsive weapons complex demands a balanced and predictive simulation infrastructure, including powerful systems like Sequoia to support Uncertainty Quantification (UQ), improving the physical models in the design codes, and more effective use of 3D models.Accomplishing this effectively will require performance at least 24 times the delivered performance of design codes today on Purple and 20 times improvement over BlueGene/L (BG/L) for underlying materials studies. The preceding performance measures represent characterizing requirements for the Sequoia system.

The critical importance of UQ for all of these mission elements stems from its systematic approach to quantifying margins and uncertainty and hence improve confidence in the predicted weapons performance. Uncertainties that are accurately quantified can be risk managed. Responsibly managed risks allow NNSA’s highest level weapons certification processes to continue with confidence.

The fundamental benefits from successful implementation of Sequoia are agile design and responsive certification infrastructure, increased accuracy in material property data, improved models for understood physical processes that are known to be important, meeting programmatic requirements for uncovering missing physics, and improving the performance of complex models and algorithms in the design codes. All of these are necessary to achieve predictive simulation in support of NNSA’s modern-responsive weapons complex.


1.2Advanced Simulation and Computing (ASC) Program Overview


The Accelerated Strategic Computing Initiative (ASCI) was established in 1995 as a critical element to help shift from test-based confidence to science- and simulation-based confidence. Specifically, ASCI was a focused and balanced program that accelerated the development of simulation capabilities needed to analyze and predict the performance, safety, and reliability of nuclear weapons and certify their functionality—far exceeding what might have been achieved in the absence of a focused initiative.

To realize its vision, ASCI created simulation capabilities based on advanced, 3D weapon codes coupled with functional, scalable high-performance computing. The result are simulations that enable assessment and certification of the safety, performance, and reliability of nuclear systems, in both 2D, and entry-level 3D simulations. The left panel of Figure 1 -1 depicts the initial goals of the first ten years of ASCI. These simulation capabilities also help scientists understand weapons aging, predict when components will have to be replaced, and evaluate the implications of changes in materials and fabrication processes to the design life of the aging weapon systems. This science-based understanding is essential to ensure that changes brought about through aging or remanufacturing will not adversely affect the enduring stockpile.

In 2000, ASCI transitioned from an initiative to a program with an enduring mission; renamed the Advanced Simulation and Computing (ASC) Program. The establishment of the ASC Program affirmed simulation and modeling as key decision-making tools and cemented their long-term role as integral components of the Stockpile Stewardship Program (SSP). The middle panel of Figure 1 -1 depicts the predictive simulation goals of SSP for the ASC Program during the lifetime of the Sequoia platform. Overall, the SSP through ASC Program:

Allows the U.S. to continue an underground nuclear test moratorium and still maintain a reliable nuclear weapons stockpile.

Ensures that all aspects of nuclear weapons stockpile operations are safe and secure— from design and engineering through dismantlement.

Generates a large return on investment by providing cost-effective, simulation-based solutions (without testing) to issues facing the nuclear weapons stockpile.





Figure 1 1: Simulation is key to eliminating the technical requirement for nuclear testing.


Lastly, as the US maintains its moratorium on underground nuclear tests, the Complex cannot continue to base its simulation and modeling efforts solely on data that are increasingly removed from the reality of the aged-weapons performance. Previously, both the limited computational tools and the near-term commitments to support the stockpile necessitated this approach. Now, however, the ASC Program has a development path for the needed software and hardware tools to move towards a quantified predictive capability (Figure 1 -2). The ASC Roadmap focuses the ASC Program’s efforts over the next decade on providing new levels of predictive capability to the SSP. It defines focus areas and supporting goals and targets required to achieve predictive capability in modeling and simulation, and it articulates a sequential, priority-based approach to achieving a new level of fidelity, adding confidence to SSP decisions and supporting a capability-based nuclear deterrent into the future.

Computer simulation is, and will continue to be, the only means to responsively address emerging issues related to systems under nuclear conditions. This continued capability is crucial to the nation’s commitment to cease underground nuclear tests. The ASC Program is following two paths that allow it to maintain the testing moratorium: the traditional path of calibrating models to underground test data and performing simulations in regimes that are minimally removed from the applicable parameter space, and the rigorous, science-based path intended to address a diverse portfolio of current and future nuclear applications.

As Figure 1 -2 illustrates, aging and refurbishment push nuclear weapons behavior into an area where the uncertainty associated with traditional approaches becomes progressively larger. To credibly address this space and predict performance further from the as-tested configurations, the ASC Program must create modern physical models with capabilities enabling confident calculation in these new and more applicable regimes.



Figure 1 2: Near-term weapons support and long-term science base.


The ASC Program has aggressively pushed computational capabilities and enhanced simulation tools to meet the needs of the SSP in the near term,. Code developers and designers have used test data to calibrate models to build effective computer representations that probe scenarios at and near the area of test experience. The process of calibration allowed for credible interpolation between different nuclear tests and for small extrapolations to untested conditions. However, this same process conceals the unknown science issues through possibly compensating errors in various approximations that mask reality.

There are several clear advantages to the replacement of calibrated models with credible scientific models:

Improved confidence in ASC Program predictions over time.

Confirmation, rather than calibration of ASC Program simulation predictions through existing nuclear test data.

Creation of a robust, responsive, and versatile simulation tool that provides uncertainty bounds with predictions.

The ASC Program has, in fewer than ten years, produced results that may well make it the most successful high-performance computing program in U.S. history. Three of the top ten systems on the June 2007 Top 5004 list of the world’s fastest computers are the ASC BlueGene/L and ASC Purple at Lawrence Livermore National Laboratory, and ASC RedStorm at Sandia National Laboratories. These systems have been instrumental in first-time, 3D simulations involving components of a nuclear weapon during an explosion. Such accomplishments are based on the successes of other elements of ASC Program research, such as scalable algorithms, programming techniques for thousands of processors, and unparalleled visualization capabilities. This history offers confidence that the challenging goals and objectives facing the ASC Program can be achieved.

As an integral and vital element of the SSP, the ASC Program provides the integrating simulation and modeling capabilities and technologies needed to combine new and old experimental data, past nuclear test data, and past design and engineering experience into a powerful tool for future design assessment and certification of nuclear weapons and their components. ASC Program capabilities are needed to model prior manufacturing processes for weapon components and define new, cost-effective, safe, and environmentally compliant manufacturing processes that will provide for consistent nuclear weapon performance, safety, and reliability in the future.

The simulation and modeling tools have already made impacts on the assessment of stockpile issues. Weapon designers, scientists, and engineers are applying ASC Program simulation and modeling capabilities and technologies to assess changes occurring in stockpile nuclear weapons due to natural aging and introduction of modifications.

The recent ASC Roadmap has provided the programmatic justification for petascale and later exascale computing requirements. The ASC Platform Roadmap responded to these programmatic drivers with a platforms roadmap that tasks the ASC Program to delivered petascale computational requirements. The present Sequoia and Dawn systems procurement is intended to deliver on this roadmap, subject to ASC Program and budgetary constraints.

As part of the ASC Roadmap, the ASC Program developed, in conjunction with the overall SSP, a set of eight High Level (Level 1) milestones (Table 1 -1) for the FY07 through FY20 timeframe. These milestones are reportable to the U.S. Congress to demonstrate progress towards predictive simulation and support of the overall NNSA 2030 transition strategy. ASC Sequoia, and the Dawn initial delivery system, will be the Production computing engine used by the program to deliver on these milestones during the lifetime of these systems.



ASC Level 1 Milestone and Title

Responsibility

End Date

Program Stakeholders

1. Develop a 100 teraFLOP/s platform environment supporting Tri-Lab Directed Stockpile Work (DSW) and Campaign simulation requirements.

HQ, LLNL

FY07

Q1


C11

2. Develop, implement, and apply a suite of physics-based models and high-fidelity databases to enable predictive simulation of the initial conditions for secondary performance.

HQ, LLNL, LANL, SNL

FY09

Q4


C11, C4

2a: Develop, implement, and validate a suite of physics-based models and high-fidelity databases in support of Full Operational Capability in DTRA's National Technical Nuclear Forensics program.

HQ, LLNL, LANL

FY09
Q4

C11, C1, C4, NA-22, DTRA

3. Baseline demonstration of UQ aggregation methodology for full-system weapon performance prediction

HQ, LLNL, LANL

FY10

Q4


C11, C1, C4, DSW

4. Develop, implement, and apply a suite of physics-based models and high-fidelity databases to enable predictive simulation of the initial conditions for primary boost.

HQ, LLNL, LANL

FY12

Q4


C11, C1, C2

5. Capabilities for SFI response improvements

HQ, LLNL, LANL, SNL

FY13

Q4


C11, DSW

6. Develop, implement, and apply a suite of physics-based models and high-fidelity databases to enable predictive simulation of primary boost

HQ, LLNL, LANL, SNL

FY15

Q4


C11, C1, C2, C10

7. Develop predictive capability for full-system integrated weapon safety assessment

HQ, LLNL, LANL, SNL

FY16

Q4


C11, C1, C2, DSW

8. Develop, implement, and apply a suite of physics-based models and high-fidelity databases to enable predictive simulation of secondary performance

HQ, LLNL, LANL

FY20

Q4


C11, C4, C2, C10

Table 1 1: Proposed ASC Level 1 Milestone List from ASC FY07 Program Plan.




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