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After more than two years of joint research, Lawrence Livermore National Laboratory (LLNL), Total and Stanford University are releasing an open-source, high-performance simulator for large-scale geological carbon dioxide (CO2) storage. The GEOSX simulator will enable researchers around the world to build on the work of the three partners, providing an open framework to accelerate the development of carbon capture, utilization and storage (CCUS) technologies.
An ongoing Technology Commercialization Fund (TCF) project with Eaton Corporation is producing great results for grid planning. IPO is proud to present this private sector collaboration at the Innovation XLab Grid Modernization Summit January 24-25.
Autopack is an open-source python tool that enables the automatic labeling of packing motifs for large and chemically diverse datasets of molecular crystals. Autopack takes advantage of geometric descriptors to find useful cross-sections within the crystal structure to elucidate the associated packing motif. Autopack is capable of processing either crystallographic information files (CIFs) or Cambridge Crystal Structure Database (CCDC) reference codes provided in a CSV file. Internally, Autopack will both validate and filter crystal structures that do not satisfy the packing motif labeling criteria (as explained in the associated publication), limiting the number of manual preprocessing steps.
LLNL is seeking industry partners to collaborate on quantum science and technology research and development in the following areas: quantum-coherent device physics, quantum materials, quantum–classical interfaces, computing and simulation, and sensing and detection.
MoonQuake is an innovative experiential learning tool for increasing awareness of inclusion and promoting cooperative behaviors, performance, and productivity. Developed by a multidisciplinary team at LLNL, MoonQuake allows participants to build inclusive behaviors through a unique learning experience based on serious gaming methodology.
To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most expansive and recently published CT data preprocessing and reconstruction algorithms available.
The OneID solution combines custom-developed code with proven commercial software to provide three core components; (1) back-end processes and administrative utilities to reconcile identity data received from multiple partners within an organization into a single unique identifier; (2) an interface that dynamically displays authentication options to the user based on the assurance level required; and (3) an Attribute Exchange Service for consumption by enterprise applications. The management of the identity remains with the source system, thus improving the accuracy and timeliness of modifications.
Customized for industrial uses, the ALE3D4I code allows a user to not only switch between the Lagrangian and Eulerian techniques but also combine the two so that the mesh “relaxes” at the leading edge of the object. The amount of relaxation is determined by the user, who can “weight” the simulation so that more zones are forced into a specific area of interest, for greater accuracy at that spot. Supporting mesh relaxation broadens the scope of applications in comparison to codes that are restricted to Lagrangian- or Eulerian-only approaches. For some applications, ALE3D4I can deliver accuracy similar to that of other simulation techniques but with as few as one-tenth the number of mesh elements.
Beyond its foundation as a hydrodynamics and structural code, ALE3D4I has multi-…
The LLNL method for optimizing as built optical designs uses insights from perturbed optical system theory and reformulates perturbation of optical performance in terms of double Zernikes, which can be calculated analytically rather than by tracing thousands of rays. A new theory of compensation is enabled by the use of double Zernikes which allows the performance degradation of a perturbed and compensated optical system to be calculated with a matrix multiplication using paraxial quantities rather than by iteration involving tracing large sets of rays. Almost no additional ray-tracing beyond that used in nominal design is required.
LLNL’s IUC system protects electronic systems from tampering and protects the electronic system’s components from unauthorized use. This is directly aimed at solving known issues in cybersecurity and electronic device counterfeiting.
LLNL’s IUC system can be programmed to enable a variety of responses at a component level and at the device level if verification of the authenticity of any components fails. The system can also be set up so for centralized management – keeping the initial set-up of the device and its controls during operations separate and managed outside of the device’s end user purview. For devices that may not require centralized controls, the IUC can be enabled to allow verified human operators to securely enable or disable the IUC system – an added…
LLNL computational scientists have developed a new technology that solves critical problems for combustion engineers and designers. LLNL’s Zero-Order Reaction Kinetics (Zero-RK) is a software package for simulating chemically reacting systems. Zero-RK’s algorithms dramatically reduce the time to results for many commercial applications, providing in some cases a three-orders-of-magnitude reduction in simulation time. Zero-RK’s feature set, including simulation of zero- and quasi-dimensional reactor systems, reaction sensitivity analyses, and coupling to CFD packages, allows users to simulate a wide variety of systems and devices. These systems include internal combustion engines for automotive and heavy-duty platforms, gas turbines, rocket engines, and industrial burners.
Autonomous systems operate in the air, on land, and even underwater. Sensing and avoiding objects is a critical necessity for autonomous vehicles when navigating their environment. Detecting objects in a vehicle's path and rapidly computing changes to the vehicle's trajectory requires object detection, path optimization, and vehicle guidance. Current solutions to this problem using optics can be limited to daylight applications, require significant system resources, and suffer in low-light or foul weather environments.
LLNL's new "Catalyst" supercomputer is now available for collaborative projects with American industry. Developed by a partnership with Cray and Intel, the novel architecture behind this high performance computing (HPC) cluster is intended to serve as a proving ground for new HPC and Big Data technologies and algorithms.
Catalyst boasts nearly a terabyte of addressable memory per compute node through the addition of 128 gigabytes (GB) of dynamic random access memory (DRAM) per node and 800 GB of non-volatile memory (NVRAM) per node in the form of PCIe high-bandwidth Intel Solid State Drives (SSD). Additionally, each Lustre router node contains 3.2 terabytes (TB) of NVRAM. Improved cluster networking is achieved with dual rail Quad Data Rate (QDR-80) Intel TrueScale fabrics.…
The Discriminant Random Forest combines advantages of several methodologies and techniques to produce lower classification error rates.
LLNL's NeMS system enables network mapping operations by using two LLNL-developed software systems: LLNL's NeMS tool and the Everest visualization system. Each software system can be also used separately for their specific applications. When the two systems are used together as an iterative analysis platform, LLNL's NeMS system provides network security managers and information technology personnel with continuing network situational awareness.
The LLNL's NeMS tool is a software-based network characterization and discovery application. The LLNL's NeMS strives to produce a comprehensive representation of IP-based computer network environments. The LLNL's NeMS supports actionable intelligence and meaningful decision making by providing a view of the actual state of a computer…
LLNL offers opportunities for joint software development to bridge the gap between the capabilities of Independent Software Vendors (ISVs) of scientific and engineering application codes and HPC users’ needs. Successful relations will yield a greater number of commercial codes available to run on HPC platforms, software with expanded features and improved performance, and wider availability and usability of existing HPC codes.
Software vendor engagements with LLNL may address such solutions as:
- Scaling vendor codes to run on HPC platforms
- Expanding vendor software capabilities with additional solver libraries and tool suites
- Increasing research discovery through algorithm development and multi-physics integration
- Optimizing code…
This technology provides algorithms that accurately localize small-arm-fire by tracking bullets from high-powered weapons, automatic rifles, rocket propelled grenades (RPGs), mortars, and similar projectiles. The software integrates commercially available infrared video cameras, processes raw imagery data, detects and tracks projectiles, and determines the location of the shooters within error bounds. The technique and algorithms have been shown to be resistant to optical clutter.