OFFICE OF ADVANCED SIMULATION AND COMPUTING AND INSTITUTIONAL R&D PROGRAMS

Welcome to the first 2025 issue of the ASC newsletter (and my first) – published quarterly to socialize the impactful work being performed by the National Nuclear Security Administration (NNSA) laboratories and our other partners.  This edition begins with a highlight from Lawrence Livermore National Laboratory (LLNL) which officially unveiled El Capitan as the world's most powerful supercomputer and first exascale system dedicated to national security.  Other featured highlights in this edition include: 

• Sandia National Laboratories’ (SNL’s) enhancements to Cubit accelerate W87-1 and W93 radiation-analysis tasks.
• Los Alamos National Laboratory’s (LANL’s) use of machine learning (ML) and the xRAGE hydrodynamics simulation code to optimize inertial confinement fusion (ICF) experiments, improving compression efficiency and increasing neutron yield by 68%.
• SNL’s quantitative validation increasing confidence in applications of the Gemma code to W87-1 and W80-4 problems.
• LLNL’s development of an all-atom molecular dynamics workflow to predict properties of insensitive high explosives (IHEs) – a method to be used this year to accelerate development of novel IHE materials.  

I’m excited to have joined NNSA and to see all the great work being done in support of the NNSA mission.  Please join me in thanking the professionals who delivered the achievements highlighted in this newsletter and on an ongoing basis, all in support of our national security mission.

Dr. Stephen Rinehart
Assistant Deputy Administrator, ASC

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LLNL’s El Capitan is verified as the world's fastest supercomputer.

LLNL, in collaboration with the NNSA, Hewlett Packard Enterprise (HPE) and AMD, officially unveiled El Capitan as the world's most powerful supercomputer and first exascale system dedicated to national security. 

Verified at 1.742 exaFLOPS (1.742 quintillion calculations per second) on the High Performance Linpack—the standard benchmark used by the TOP500 organization to evaluate supercomputing performance—El Capitan is the fastest computing system ever benchmarked.  El Capitan also registered a peak theoretical performance of 2.79 exaFLOPS.  The TOP500 ranking was announced at Supercomputing 2024 (SC24), the leading high-performance computing (HPC) conference, held in Atlanta last November. 

As NNSA’s first exascale supercomputer, El Capitan is a premier resource for the NNSA Tri-Labs (LLNL, LANL, and SNL) to advance nuclear weapon science and scientific discovery, providing the vast computational power necessary to ensure the safety, security, and reliability of the Nation's nuclear deterrent without nuclear testing. This state-of-the-art system marks a monumental leap forward in HPC, enabling an unprecedented modeling and simulation capability essential for NNSA’s Stockpile Stewardship Program, which certifies the U.S. nuclear stockpile, and other critical nuclear security missions such as nonproliferation and counterterrorism. 

NNSA Tri-Lab scientists will utilize El Capitan’s speed and unparalleled capabilities to further advance NNSA’s core mission of maintaining an aging stockpile while simultaneously pursuing weapon modernization, such as the W87-1 and W93 warheads currently under development.  El Capitan will realize a multi-decade goal to model weapon performance and safety in high-fidelity resolution with quantified uncertainties using codes that have been developed and tuned for exascale over the past decade.  It also will be used to model advanced high-energy-density physics experiments (such as inertial confinement fusion), model the complex dynamics of ballistic reentry, and aid a more detailed understanding of material behavior under extreme conditions.

El Capitan will also support novel new artificial intelligence (AI) based workflows to address emerging challenges in NNSA’s mission, including material discovery, design optimization, advanced manufacturing, digital twins, and intelligent AI assistants trained on classified data.  Advances in these national security capabilities will impact the broader mission of the DOE and the scientific community at large, including energy, seismic modeling, and building a more efficient and agile enterprise based on advanced computing. 

“This tremendous accomplishment, years in the making and the result of tireless efforts by hundreds of dedicated employees in this large collaborative team, is a testament to the Laboratory’s leadership in driving scientific discovery.  It continues a legacy of supercomputing excellence that spans more than 70 years,” said LLNL Lab Director Kim Budil.  “El Capitan’s extraordinary computing capabilities will allow us to tackle complex challenges that were previously out of reach.  We are proud to lead this achievement in partnership with industry, and advance science in ways that will benefit society and the Nation as a whole." 

With more than 11,000 compute nodes and 5.4375 petabytes of total memory, El Capitan represents a more than 20-fold peak increase in computing performance over LLNL’s previous most powerful system, Sierra, which has a peak performance of 125 petaFLOPS.  Complex, high-resolution 3D simulations that would take weeks or months on Sierra will be done in just hours or days on El Capitan, leading to previously unimaginable insights, according to LLNL experts. 

“We expect El Capitan to make yesterday’s ‘hero runs’ of large-scale 3D models commonplace, allowing us to analyze components of the stockpile in greater detail and with more precision than ever before,” said LLNL’s Weapon Simulation and Computing Program Director Rob Neely.  “El Capitan will enable us to simulate entire weapons systems, incorporating various real-world factors such as materials, manufacturing imperfections, and environmental variables, meaning more accurate predictive capabilities and better-informed decision making for NNSA and the Stockpile Stewardship Program.” 

Built on the HPE Cray Supercomputing EX system specifically designed for exascale computing, El Capitan is a product of numerous cutting-edge innovations in HPC.  It features HPE’s direct liquid-cool leadership-class solutions, including the HPE Slingshot interconnect, alongside custom-built, ultra-fast, near-node local storage tiered to a global Lustre file system that’s shared between all the compute nodes.  It capitalizes on an end-to-end approach, encompassing everything from system architecture and data storage to networking and software.  El Capitan is powered by the AMD Instinct MI300A accelerated processing units (APUs), which combine central processing unit (CPU) cores, graphics processing unit (GPU) cores, and high-bandwidth memory into a single shared package (Figure 3). 

Designed to optimize the convergence of AI and HPC, El Capitan’s AMD Instinct MI300A APUs deliver unmatched computational performance, energy efficiency, and reliability, and are well suited for work in support of modeling and simulation workloads that will impact national nuclear security, as well as efforts in fusion energy, drug discovery, and other areas (read more about El Capitan in the recent LLNL news release).

 

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SNL Cubit enhancements accelerate W87-1 and W93 radiation-analysis tasks.

Thermomechanical shock (TMS), thermostructural response (TSR), and system generated electromagnetic pulse (SGEMP) effects related to hostile x-ray encounters must be assessed for strategic systems like the W87-1 and W93.  The first step in these analyses is the highly time-consuming process of generating a simplified representation of the complex internal geometries of a weapon system from a computer-aided design (CAD) model.  Thanks to a new primitive creation functionality in Cubit® that instantly performs calculations related to CAD model dimensions, analysts can now obtain x-ray energy deposition distributions needed for TMS, TSR, and SGEMP analysis in a day instead of weeks (adapted from SAND2024-16000M).

 

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LANL scientists used supervised machine learning and the xRAGE hydrodynamics simulation code to optimize inertial confinement fusion experiments with a cylindrical target.

Their design improved compression efficiency and increased neutron yield by 68%.

The study of inertial confinement fusion (ICF) implosions advances our understanding of the science relevant to stockpile stewardship.  In an ICF implosion, the rocket effect drives a spherical shell inward, compressing and heating a central deuterium-tritium (DT) gas to reach conditions relevant to runaway thermonuclear fusion.  However, the features of an ICF target can induce hydrodynamic instability growth and impact implosion performance.  As a result, validating and improving computational models of ICF implosion dynamics, coupled with experimental ICF data, strengthens confidence in our capability to predict performance.

Assessing hydrodynamic instability growth in standard spherical systems poses challenges.  Imaging through a spherical shell requires analysis methods with potentially ambiguous interpretations, and cone-in-shell experiments are limited to low convergence.  Therefore, a cylindrical platform that allows for direct diagnostic access while maintaining geometric convergence effects is needed.  Previous cylindrical implosion experiments [1-2] have used annular plastic cylinders with a central low-density foam fill as targets and examined the growth of pre-imposed periodic features, using foam density to control the deceleration history and the final convergence of the target.  Still, running high-fidelity simulations to fully capture the instability growth in these geometries remains computationally expensive, further complicating the design of these targets for experimental validation.

In a recent paper [3], LANL scientists leveraged supervised machine learning (ML) techniques, specifically Gaussian processes, deep jointly-informed neural networks, and their Bayesian counterpart, Bayesian deep jointly-informed neural networks, to optimize cylindrical target design over a large parameter space.  Initial efforts trained these surrogate models on a dataset generated from one-dimensional (1D) runs of LANL’s Eulerian radiation-hydrodynamics code xRAGE simulations, providing low-fidelity predictions of thermonuclear yield as a function of various design parameters (e.g., ablator thickness, DT fuel density). 

ICF hotspot conditions are better reproduced by replacing the central foam fill of the target with DT gas, allowing detailed studies on how the mixing of shell material into the DT fuel impacts thermonuclear burn.  Bayesian optimization was used to iteratively refine design parameters, selecting those that maximized yield while adhering to predefined constraints.  When expanded to include laser pulse-shape parameters, as shown in Figure 5, ML surrogate models identified a design that improved compression efficiency and increased neutron yield by 68% compared to a square-pulse laser drive.

The team is now integrating 2D simulations into a cost-aware, multi-fidelity optimization framework to account for instability affects more accurately, as past studies show that optimal designs in 1D do not always perform similarly in more complex simulations.  Multi-fidelity optimization routines integrate data from various simulation fidelities, ensuring accurate performance prediction at a reduced computational expense (LA-UR-24-31406). 

References:
[1]    S. Palaniyappan, J. P. Sauppe, B. J. Tobias, C. F. Kawaguchi, K. A. Flippo, A. B. Zylstra, O. L. Landen, D. Shvarts, E. Malka, S. H. Batha, P. A. Bradley, E. N. Loomis, N. N. Vazirani, L. Kot, D. W. Schmidt, T. H. Day, R. Gonzales, and J. L. Kline, “Hydro-scaling of direct-drive cylindrical implosions at the OMEGA and the National Ignition Facility,” Phys. Plasmas 27, 042708 (2020).
[2]    J. P. Sauppe, S. Palaniyappan, B. J. Tobias, J. L. Kline, K. A. Flippo, O. L. Landen, D. Shvarts, S. H. Batha, P. A. Bradley, E. N. Loomis, N. N. Vazirani, C. F. Kawaguchi, L. Kot, D. W. Schmidt, T. H. Day, A. B. Zylstra, and E. Malka, “Demonstration of scale-invariant Rayleigh-Taylor instability growth in laser-driven cylindrical implosion experiments,” Phys. Rev. Lett.  124, 185003 (2020).
[3]    W. P. Gammel, J.P. Sauppe, P. Bradley, “A Gaussian process based surrogate approach for the optimization of cylindrical targets,” Phys. Plasmas 1 31, 072705 (2024). 

 

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Quantitative validation increases confidence in applying Gemma code to W87-1 and W80-4. 

Gemma is a computational electromagnetic radiation (EMR) code designed to simulate three-dimensional scattering and coupling problems using various models, including SNL-developed narrow-slot models. These types of models are not available in commercial codes and are used to capture electromagnetic-relevant geometric features in the W87-1 and W80-4.  To simulate cavity coupling problems for these systems, slot models are essential for representing the coupling between the electromagnetic environment and internal cavities. In a quantitative validation assessment against experimental data for a prototypical tortuous-path slot, SNL researchers found that Gemma captured two principal slot coupling resonance peaks in both frequency and magnitude (with differences to experimental data of 6.2% and 0.18% in frequency and 17% (1.33 dB) and 32% (3.39 dB) in magnitude) for a non-trivial slot geometry as well as captured the trend over a wide range of frequency (over an order of magnitude) and electric field (roughly three orders of magnitude). The model successfully reproduced the experimental results, allowing users to apply Gemma to W87-1 and W80-4 geometries with increased confidence, which reduces the overall risk in using modeling and simulation for qualification activities and eliminates the need for costly over-conservatism in the environmental specification that would otherwise be needed to compensate for the unvalidated tortuous slot model (adapted from SAND2024-15262N). 

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LLNL Physics and Engineering Materials scientists developed an all-atom molecular dynamics workflow to predict properties of insensitive high explosives (IHEs) using parameters optimized for specific HE-polymer pairs.

The method will be used this year to accelerate development of novel IHE materials.

Insensitive high explosives (IHEs) have historically been formulated by combining a base high explosive (HE) with a polymer binder.  A computational model that can predict favorable HE-polymer pairs based on fundamental chemical information would help narrow the design space for alternative IHE formulations with higher energy output and good safety. To this end, LLNL Physics and Engineering Materials scientists developed an all-atom classical molecular dynamics (MD) modeling workflow to predict adhesion energy for HE-polymer pairs.  

A recently published MD force field for a novel HE was implemented and validated against experimental data. The validated model was then applied to rank the thermodynamic favorability of specific crystal surface facets to constrain future MD simulations of polymer wetting. Finally, a recently developed approach to test and refit classical MD force fields against high-level density functional theory (DFT) calculations was applied to obtain optimized parameters for specific HE-polymer pair adhesion. The optimized force field model and HE-polymer interface modeling workflow will be applied to predict adhesion of novel IHE formulations in FY25. It is expected that this work will accelerate the development of new IHE materials by rationally choosing HE-polymer pairs with good adhesion (LLNL-ABS-2000796).

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Artificial Intelligence Breakthrough: LANL leverages cutting-edge AI technology to support an ASC code.

AI experts at LANL have successfully restructured and tokenized an AI-ready data set from LLNL to finetune a large language model (LLM) with 70 billion parameters.  Researchers used SambaNova’s advanced AI hardware to take on the job.  This high-performance hardware, located onsite at LANL (see Figure 7), is part of a strategic partnership with SambaNova, an AI solutions provider. The collaboration explores using LLMs to translate computer codes between programming languages to improve maintainability and assure long-term software sustainability for the national security mission of the NNSA laboratories.  Support for advancing ASC integrated codes is critical to the Stockpile Stewardship Program (LA-UR-24-32260).

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SNL advances NNSA neuromorphic computing expertise at the International Conference on Neuromorphic Systems. 

The International Conference on Neuromorphic Systems (ICONS) is a leading neuromorphic computing meeting that engages the academic, industry, and research communities.  The goal of ICONS is to bring together leading researchers in neuromorphic computing to present new research, develop new collaborations, and provide a forum to publish work.  SNL co-organized the 2024 ICONS (held in a hybrid format, with the physical conference in Arlington, VA July 30-August 2, 2024) and represented work through publications, government partner engagement, and tutorials.  Presentations highlighted SNL perspectives, applications, and tools developed in neuromorphic computing.  

With neuromorphic computing maturing as a computing paradigm, SNL leadership helps shape outcomes across the software stack to applications.  In doing so, maturation of next-generation HPC approaches can be assessed and implemented (SAND2024-16000M).

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Integrating experimental data into modern workflows: NSDSConnect, a toolkit to integrate organized experimental data into LANL’s Common Modeling Framework.

The LANL weapons-modeling community has long desired a computational infrastructure that enables a modeler to set up and run a problem, compare the computational output to pedigreed experimental data, and then archive that model and its comparison to experimental data.  However, until now, simulation workflows could not connect to a centralized database that curates, labels, and shares experimental data in a tractable method.  Over the past several years, two separate frameworks were built independently: the Common Modeling Framework (CMF) was developed by LANL’s design community to provide the simulation workflow needed; and the National Security Data Solution (NSDS) has come online as the LANL weapons data management tool that will eventually provide access to decades worth of experimental data. A FY24 Level 2 Milestone solved the tractability of experimental data in simulation workflows by connecting CMF’s simulation workflows with NSDS experimental data, a watershed moment.

NSDSConnect, a tool that provides a straightforward methodology to access and query NSDS experimental data that was collected and curated for the LANL Weapons program, was released for this milestone.  The tool can also retrieve metadata from the NSDS data records within a simulation workflow and can include it in the pedigree.  Now it is possible to share CMF problem setups that automate the comparison of computational outputs with experimental data.  A proof-of-concept workflow, based on using HE data for simulation validation, shows how a user can compare experimental and simulation data (Figure 9).  This workflow provides feedback on the accuracy of HE material models and paves the way for CMF users to interact directly with experimental data through NSDS. 

The NSDSConnect milestone will provide for a much stronger and easier-to-use integrated computational methodology that directly connects simulations to the experimental data used to validate those simulations.  This is an integral part of the Verification and Validation (V&V) program and part of modernizing the V&V workflows that give LANL confidence in its simulation capabilities (LA-UR-24-32260). 

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SNL held a successful Predictive Engineering Science Panel meeting in October 2024.

The Predictive Engineering Science Panel (PESP) meeting was held in Albuquerque, NM October 21-24, 2024.  SNL management and staff presented ongoing efforts to support the Laboratories’ strategic goal to lead in modern engineering with initiatives in Accelerated Model Development (AMD) and modeling and simulation for combined environments.  SNL ASC leadership appreciated all those that presented and participated in the event. Six panel members provided the SNL ASC program with findings and recommendations for SNL’s path forward.  The six panel members were:

• Scott Holswade, Chair, Director Emeritus, Sandia National Laboratories
• Steven Bauer, NASA Langley Research Center
• Iain Boyd, Aerospace Engineering Sciences, University of Colorado Boulder
• Tom Cwik, NASA Jet Propulsion Laboratory, California Institute of Technology
• Shawn Dirk, Sandia National Laboratories
• Dan Meiron, Applied and Computational Mathematics, California Institute of Technology

The panel members gave positive feedback on the strategy, noting that the SNL ASC program was moving in the right direction.  One key recommendation included identifying mechanisms to partner more effectively with systems and component Product Realization Teams (PRTs) to guide testing and the modeling and simulation strategy for design and qualification.  The panel members communicated their recommendations in an out-brief and created a final report (SAND2025-00315) distributed to relevant ASC, Science & Technology (S&T), and Nuclear Deterrence (ND) leadership.  Questions regarding the PESP meeting can be directed to David Littlewood at djlittl [at] sandia.gov (djlittl[at]sandia[dot]gov) (SAND2024-15262N).

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LANL’s development of interatomic potentials aids in materials discovery for extreme environments.

Many applications important to the NNSA mission require metal alloys to survive thermal and mechanical extremes.  The tradeoffs in material properties associated with conventional alloys are generally well understood in the context of these extreme environments.  The discovery of new materials promises to enable technology advancements beyond what can be achieved with standard alloy compositions. 

High entropy alloys (HEAs) and multi-principal element alloys (MPEAs) are designer materials of much interest because of their remarkable ability to combine high strength and ductility.  A HEA is a complex metal alloy comprising five or more elements in near-equal proportions, designed to maximize atomic diversity and configurational entropy, resulting in unique mechanical and thermal properties such as high strength, toughness, and resistance to wear, corrosion, and oxidation.  MPEAs are similar to HEAs but can be comprised of three or more elements in near-equal proportions. 

Because HEAs and MPEAs are mixtures of multiple elements, the composition space that must be explored to optimize properties is large.  This means that examining the design space to discover optimal HEA/MPEA compositions using experimental methods alone is intractable.  Atomistic modeling using classical molecular dynamics can help accelerate the identification of compositions that are well-suited for experimental characterization.  This modeling approach enables specified numbers of atoms from multiple elements to be placed in high-entropy configurations and then computes the resulting material properties and deformation mechanisms.  The accuracy of these calculations depends on the quality of the interatomic potentials used to compute the energy and resulting forces associated with configurations of atoms. 

Recent work by ASC Physics and Engineering Models (PEM) developed a multi-component Modified Embedded Atom Method (MEAM) potential for HEAs, consisting of a mixture of niobium, tantalum, titanium, vanadium, and zirconium (i.e., NbTaTiVZr, including all subsets such as NbTaTiV).  Niobium, tantalum, and vanadium each exhibit body-centered cubic (BCC) symmetry of their atomic lattice, while zirconium and titanium stabilize in a hexagonal close-packing geometry at room temperature and BCC at elevated temperatures.  BCC crystal symmetry is an atomic lattice arrangement with one atom positioned at each corner of a cube and an additional atom located at the center of the cube.  This geometry results in a high-symmetry structure that is often associated with materials having high strength and resistance to deformation. 

Using the newly developed interatomic potential, molecular dynamics simulations of selected NbTaTiVZr and NbTaTiV compositions exhibited deformation behavior that was dominated by edge dislocations, unlike pure BCC elements (e.g., Ta) in which screw dislocations prevail.  Although this work was specifically focused on the deformation behavior of a subclass of HEAs, it developed a foundation for the high-throughput development of new, accurate interatomic potentials, which will enable application of molecular dynamics to a broad range of materials modeling problems, including understanding deformation mechanisms in alloys with multiple impurities (e.g., from manufacturing or aging) (LA-UR-24-33168).

 

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Welcome Aboard…

LLNL ASC program

Jon Belof has been appointed as LLNL’s AI for Nuclear Deterrence Coordinator, reporting to the LLNL ASC Director.  Since joining LLNL in 2010 as a primary designer, he has led major initiatives, including the Equation of State program (2018–2021) and an AI/ML initiative in 2020 that laid the foundation for current activities.  From 2018 to 2025, Jon led a team of 30 scientists in the Materials Science Division and served as principal investigator (PI) for multiple high-impact projects in Strategic Deterrence and Global Security.  A Presidential Early Career Award for Scientists and Engineers (PECASE) recipient and Kavli Fellow, he has authored over 130 research papers and delivered more than 100 invited talks.  Jon is also a senior leader in the LLNL AI Community of Practice and an advisor to the LLNL Physical and Life Sciences Principal Associate Director.

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LANL ASC program

Allyson Timko is a Staff Scientist working within the Verification and Validation (V&V) subprogram at LANL.  Her verification work developing a 2D method-of-characteristics solver assesses numerical accuracy of complex flows around curved geometries.  She is also engaged in modeling and validating flyer plate experiments with materials of particular relevance to the NNSA with a focus on equation of state models.  Allyson holds bachelor’s degrees in Physics and Planetary Science, and a master’s degree in Aerospace Engineering, all from the University of Colorado, Boulder.  In her free time she enjoys hiking, dancing, and rock climbing.  

 

Tolulope (Tolu - “toe-loo”) Olatunbosun is a Data Scientist and AI Operations Developer working in the Facility Operations and User Support (FOUS) subprogram at LANL.  He graduated with a Bachelor’s of Arts in Mathematics from SUNY Geneseo and a Master’s of Science in Data Science from the Rochester Institute of Technology.  He currently works on a large language model (LLM) that will serve consultants at LANL in resolving questions in short periods of time without the direct help of staff or resorting to reading documentation.  In his time away from work, he is a drone pilot and enjoys 3D printing and tinkering with generative AI applications.  He is also classically trained in ballroom dancing!

 

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SNL ASC program

Marissa Adams received her Ph.D. in Physics from the University of Rochester in 2022.  Marissa’s current project within the ASC program is to team with the Shock Unification effort within the facilitator board.  In this role, Marissa hopes to refine the verification suite of the new Sandia Multi-physics/architecture Adaptive Shock Hydrodynamics (SMASH) code.  In her personal time, Marissa enjoys crafting, learning new languages, swimming, and leisurely hiking.  She is also expecting her first child in early April.

 

Aaron Krueger received his Ph.D. in Nuclear Engineering from Texas A&M University in December 2020.  He has been with the SNL ASC program for over five years.  Recently, Aaron took on a PI role in the ASC V&V portfolio, focusing on verification, validation, and uncertainty quantification (VVUQ) research and development for the RAMSES (Radiation Analysis Modeling and Simulation of Electrical Systems) software suite.  This work will directly support qualification and design efforts for the W80-4, W87-1, and W93.  In his free time, Aaron enjoys snowboarding, camping, and hiking.

 

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NNSA LDRD/SDRD Quarterly Highlights

 

LLNL LDRD: Evaluating trust and safety of large language models.

Amid the skyrocketing popularity of large language models (LLMs), researchers at LLNL are taking a closer look at how these AI systems perform under measurable scrutiny.  LLMs are generative AI tools trained on massive amounts of data in order to produce a text-based response to a query.  This technology has the potential to accelerate scientific research in numerous ways, from cybersecurity applications to autonomous experiments.  But even if a billion-parameter model has been trained on trillions of data points, can we still rely on its answer?  Two LLNL co-authored papers examining LLM trustworthiness — how a model uses data and makes decisions — were accepted to the 2024 International Conference on Machine Learning, one of the world’s prominent AI/ML conferences.  “This technology has a lot of momentum, and we can make it better and safer,” said Bhavya Kailkhura, who co-wrote both papers (read more in the LLNL article).

 

SNL LDRD: Using cellphone signals to protect aircraft against Global Positioning System (GPS) outages.

Dangling from a weather balloon 80,000 feet above New Mexico, a pair of antennas stick out from a Styrofoam cooler.  These antennas listen for signals that could make air travel safer.  Researchers from SNL and Ohio State University are taking experimental navigation technology to the skies, pioneering a backup system to keep an airplane on course when it cannot rely on global positioning system satellites.  More than 15 miles below the floating cooler, cell phone towers emit a steady hum of radio frequency waves.  Hundreds of miles above, non-GPS communications satellites do the same.  The idea is to use these alternative signals to calculate a vehicle’s position and velocity (read more in the SNL article).

 

LANL LDRD: Flourishing forensics - new technology helps determine the origin and history of hazardous material.

Nuclear forensic scientists analyze samples of radioactive material to better understand nuclear activities, such as the detonation of a nuclear device, the proliferation of nuclear material, or changes to a nuclear facility that go beyond what was previously disclosed in treaties.  At LANL, scientists work continually to enhance nuclear forensics capabilities.  The work of three LDRD projects was recently highlighted, sharing how the researchers leading these projects have been able to forward nuclear forensics work as a whole through their ability to analyze more types of samples quickly and accurately (read more in the LANL article online).

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Questions? Comments? Contact Us.

ASC Assistant Deputy Administrator: stephen.rinehart [at] nnsa.doe.gov (Dr. Stephen Rinehart)

ASC Deputy Assistant Deputy Administrator: thuc.hoang [at] nnsa.doe.gov (Thuc Hoang)

Program Director for Computing: simon.hammond [at] nnsa.doe.gov (Si Hammond)

Program Director for Simulation: anthony.lewis [at] nnsa.doe.gov (Anthony Lewis)

• Integrated Codes: james.peltz [at] nnsa.doe.gov (Jim Peltz)
• Physics and Engineering Models: robert.spencer [at] nnsa.doe.gov (Robert Spencer)
• Verification and Validation/PSAAP/CSGF: david.etim [at] nnsa.doe.gov (David Etim)
• Capabilities for Nuclear Intelligence: anthony.lewis [at] nnsa.doe.gov (Anthony Lewis)
• Computational Systems and Software Environment: simon.hammond [at] nnsa.doe.gov (Si Hammond), sara.campbell [at] nnsa.doe.gov (Sara Campbell)
• Facility Operations and User Support: michael.lang [at] nnsa.doe.gov (K. Mike Lang)
• LDRD/SDRD: anthony.lewis [at] nnsa.doe.gov (Anthony Lewis)