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This document is a system administrator guide for the Watchmen tool. It describes how system administrator should install the application components and configure the host system.
Computer vision models have great potential as tools for international nuclear safeguards verification activities, but off-the-shelf models require fine-tuning through transfer learning to detect relevant objects. Because open-source examples of safeguards-relevant objects are rare, and to evaluate the potential of synthetic training data for computer vision, we present the Limbo dataset. Limbo includes both real and computer-generated images of uranium hexafluoride containers for training computer vision models. Here, we generated these images iteratively based on results from data validation experiments that are detailed here. The findings from these experiments are applicable both for the safeguards community and the broader community of computer vision research using synthetic data.
A FORTRAN IV computer program used to perform modal synthesis of structures by stiffness coupling, using the dynamic transformation method is described. The program was named SCAMP (Stiffness Coupling Approach Modal-Synthesis Program). The program begins with the entry of a substructure's physical mode shapes and eigenvalues or a substructure's mass and stiffness matrix. If the mass and stiffness matrices are entered, the eigen problem for the individual substructure is solved. Provisions are included for a maximum of 20 substructures which are coupled by stiffness matrix springs. Each substructure has a number degrees of freedom (DOF), except that for DOF greater than 100; vector sets having maximum row and column size of 100 were generated prior to entering SCAMP. The substructures are then coupled together via coupling springs, and the dynamic transformation is used to reduce the size of the eigen problem.
Measurements were made of the boundary layers and wakes about a highly loaded, double-circular-arc compressor blade in cascade. These laser Doppler velocimetry measurements have yielded a very detailed and precise data base with which to test the application of viscous computational codes to turbomachinery. In order to test the computational codes at off-design conditions, the data were acquired at a chord Reynolds number of 500,000 and at three incidence angles. Moreover, these measurements have supplied some physical insight into these very complex flows. Although some natural transition is evident, laminar boundary layers usually detach and subsequently reattach as either fully or intermittently turbulent boundary layers. These transitional separation bubbles play an important role in the development of most of the boundary layers and wakes measured in this cascade and the modeling or computing of these bubbles should prove to be the key aspect in computing the entire cascade flow field. In addition, the nonequilibrium turbulent boundary layers on these highly loaded blades always have some region of separation near the trailing edge of the suction surface. These separated flows, as well as the subsequent near wakes, show no similarity and should prove to be a challenging test for the viscous computational codes.
The NASA Lewis Research Center (LeRC), established in 1941, is one of ten NASA research centers in the country. It is situated on 350 acres of land in Cuyahoga County and occupies more than 140 buildings and over 500 specialized research and test facilities. Most of LeRC's facilities are located in the City of Cleveland; some are located within the boundaries of the cities of Fairview Park and Brookpark. LeRC is a lead center for NASA's research, technology, and development in the areas of aeropropulsion and selected space applications. It is a center of excellence for turbomachinery, microgravity fluid and combustion research, and commercial communication. The base research and technology disciplines which serve both aeronautics and space areas include materials and structures, instrumentation and controls, fluid physics, electronics, and computational fluid dynamics. This study investigates LeRC's economic impact on Northeast Ohio's economy. It was conducted by The Urban Center's Economic Development Program in Cleveland State University's Levin College of Urban Affairs. The study measures LeRC's direct impact on the local economy in terms of jobs, output, payroll, and taxes, as well as the indirect impact of these economic activities when they 'ripple' throughout the economy. To fully explain LeRC's overall impact on the region, its contributions in the areas of technology transfer and education are also examined. The study uses a highly credible and widely accepted research methodology. First, regional economic multipliers based on input-output models were used to estimate the effect of LERC spending on the Northeast Ohio economy. Second, the economic models were complemented by interviews with industrial, civic, and university leaders to qualitatively assess LeRC's impact in the areas of technology transfer and education.
A computational flow field analysis is presented of the effect of core cowl porosity on shock cell strength for a modern separate flow nozzle at cruise conditions. The goal of this study was to identify the primary physical mechanisms by which the application of porosity can reduce shock cell strength and hence the broadband shock associated noise. The flow is simulated by solving the asymptotically steady, compressible, Reynoldsaveraged Navier-Stokes equations on a structured grid using an implicit, up-wind, flux-difference splitting finite volume scheme. The standard two-equation k - epsilon turbulence model with a linear stress representation is used with the addition of a eddy viscosity dependence on total temperature gradient normalized by local turbulence length scale. Specific issues addressed in this study were the optimal area required to weaken a shock impinging on the core cowl surface and the optimal level of porosity and placement of porous areas for reduction of the overall shock cell strength downstream. Two configurations of porosity were found to reduce downstream shock strength by approximately 50%.
Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.
This is a presentation to be made at the University of Tokyo. The Idaho National Laboratory is creating the next-generation reliability- and risk-assessment methods and tools that support risk-informed decision-making by combining physics-based models with probabilistic quantification approaches. Integrating these two worlds of physics and probability using a simulation framework leads us to predictions based upon an approach called “computational risk assessment” which will serve as the technical basis for the future of reliability and risk approaches. The driving factors for this new approach includes: temporal (timing issues), spatial (location issues), mechanistic (physics issues), and topological (complexity issues). By combining phenomenology directly with stochastic quantification, we are able to perform advanced uncertainty analysis directly on both parameters and models. In addition, new tools allow for the creation of reduced order models that mimic high-fidelity engineering models while still permitting the realization of thousands of simulation iterations in short run-times on current workstation-class computers. While these advanced methods and tools can provide increased realism in our engineering safety and risk approaches, their greater benefit is to provide a risk-informed engineering framework for design and operation. This talk will briefly review the history of risk assessment and risk-informed applications, describe some of the current research and development found at the Idaho National Laboratory, and discuss potential future applications and approaches for advanced methods and tools.
Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.
Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.
Shaped emitters are of interest to a broad range of applications in vacuum electronic devices. In particular, thermionic energy converters (TECs) take advantage of shaped emitters to increase the local surface field, thereby extracting more current for a given cathode temperature and applied voltage. However, modeling these devices is challenging; Warp [J.-L. Vay, D. P. Grote, R. H. Cohen, and A. Friedman, Comput. Sci. Discov. 5, 014019 (2012)] is a fully 3D particle-in-cell code capable of handling a wide range of physics problems and is well suited to modeling TECs. Additionally, recent improvements to Warp have enabled the accurate modeling of emitters with arbitrary curved surfaces. Specifically, the inclusion of subgrid resolution for computing the electrostatic potential and the ability to apply mesh refinement for specific areas of interest allow for a more accurate solution to the fields on these surfaces. These improvements coupled with Warp’s ability to handle variable particle weights make it an ideal candidate for simulating these complex devices. In this paper, the authors study the applicability of different subgrid configurations for simulating shaped emission surfaces and field convergence for different mesh-refinement techniques. They then implement a custom weighting algorithm that allows for uniform sampling of emission surfaces with a large variation in the surface electric field. They then use this algorithm to study emission for curved emitters in both the field-enhancement regime and the space-charge regime.
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does not require major manual analysis and rewriting of the code, even for very complex programs like simulations of particle-detector interactions in high-energy physics. However, the pathwise derivative estimator can be biased if there are discontinuities in the program, which may diminish its value for applications. This work integrates algorithmic differentiation into the electromagnetic shower simulation code HepEmShow based on G4HepEm, allowing us to study how well pathwise derivatives approximate derivatives of energy depositions in a sampling calorimeter with respect to parameters of the beam and geometry. We found that when multiple scattering is disabled in the simulation, means of pathwise derivatives converge quickly to their expected values, and these are close to the actual derivatives of the energy deposition. Additionally, we demonstrate the applicability of this novel gradient estimator for stochastic gradient-based optimization in a model example.
Softwarization of networked infrastructures combined with containerization of codes promises unprecedented computing capabilities distributed across the federations of computing systems and physical instruments. The development and testing of a software stack that implements these capabilities over an expensive physical production infrastructure is not cost-effective, and in the early stages, may potentially cause service disruptions. To address these aspects, we develop the Virtual Federated Science Instrument Environment (VFSIE), a digital twin of the physical infrastructure that emulates a multi-site federation. Each federated site is emulated using containers and virtual hosts that are connected over local-area networks, and the sites, in turn, are connected over an emulated wide-area network. We describe the framework design and implementation details. We also illustrate its application by emulating a federation of four laboratories that use Jupyter Notebook for computations and the EPICS software system for instrument control.
Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. Here, in this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in these systems and their relationship to ORR activity and stability based on dual transitional metal centers. These consists of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co-sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibits lowest (< 4%), and Ni demonstrates highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in-situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for tunable design and future development of these systems for practical electrochemical applications.
To meet the increasing demand for electricity and to have a more reliable and resilient electric grid against conventional and extreme events, grid modernization is more crucial now than ever before. This will require the development and deployment of devices that provide advanced communication capabilities. The overall efficiency, reliability, and resilience of the smart grid will be inextricably linked to the exchange of information between these devices. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. While a smarter grid will depend critically on information flow, these benefits will be accrued only if that information can be protected. Nowadays, information is secured in smart grids primarily through cryptography. However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. The security of QKD stems ultimately from the very nature of quantum physics. In this paper, we investigate the applicability of QKD to the various smart grid sectors and specific use cases. We have identified 18 smart grid use cases of interest for QKD suitability together with 7 QKD factors used for the assessment of the various use cases. For each use case, the impact to security of the loss of confidentiality, integrity, and/or availability is specified. In addition, the suitability of QKD is assessed for each use case with respect to multiple factors.
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.