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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Parallel-in-Time Simulation of Lindblad's Equation

Constructing fast quantum logic gates is critical to building a scalable quantum computer. We consider a qudit, a quantum version of a bit that can take an arbitrary number of states, coupled with a cavity. In this project, we wish to force the qudit to reach the 0-state, for any possible initial state. The coupled system changes in time according to Lindblad’s equation, an ordinary differential equation on the density matrix of the quantum system. Lindblad’s equation contains some parameters that we can control, so-called control functions. We seek control functions which force the qudit to the 0-state within 2 microseconds, which is much faster than what is currently done in practice. The search method is gradient descent, a numerical optimization method that uses gradient information to iteratively improve the control parameters. My contribution to this project is an attempt to speed up the computation of the gradient. It currently takes about 40 seconds to compute the gradient which involves solving a set of ODEs sequentially. Current supercomputers have thousands of cores, but sequential computations can only make use of 1 core at a time. We wish to divide up the work better, so that we can use many more cores at once. To this end, we have implemented the Multigrid Reduction in Time (MGRIT) algorithm. We perform a systematic parameter search on how to best apply this algorithm. Results indicate a 25 percent speed up for solving Lindblad’s equation and determining how close the final state is the 0-state.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Liquid-Rich Shale Potential of Utah’s Uinta and Paradox Basins: Reservoir Characterization and Development Optimization

The enclosed report is the culmination of a multi-year and multi-faceted research project investigating Utah’s unconventional tight oil potential. From the beginning, the project team focused efforts on two different plays: (1) the basal Green River Formation’s (GRF) Uteland Butte unconventional play in the Uinta Basin and (2) the more established but understudied Cane Creek shale play in the Paradox Basin. The 2009-2014 high price of crude oil, coupled with lower natural gas prices, generated renewed interest in exploration and development of liquid hydrocarbon reserves. Following the success of the mid-2000s shale gas boom and employing many of the same well completion techniques, petroleum companies started exploring for liquid petroleum in shale formations. In fact, many shales targeted for natural gas include areas in which the shale is more prone to liquid production. In Utah, organic-rich shales in the Uinta and Paradox Basins have been the source of significant hydrocarbon generation, with companies traditionally targeting the interbedded sands or carbonates for their conventional resource recovery. Because of the advances in horizontal drilling and hydraulic fracturing techniques, operators in these basins started to explore the petroleum production potential of the shale units themselves. The GRF in the Uinta Basin has been studied for over 50 years, since the first hydrocarbon discoveries. However, those studies focused on the many conventional sandstone reservoirs currently producing oil and gas. In contrast, less information was available about the more unconventional crude oil production potential of thinner carbonate/shale units, most notably the basal Uteland Butte member. The Cane Creek shale of the Paradox Basin has been a target for exploration periodically since the 1960s and produces oil from several small fields. The play generated much interest in the early 1990s with the successful use of horizontal drilling. Recently, the USGS assessed the undiscovered oil resource in the Cane Creek shale of the Paradox Basin at 103 million barrels at a 95 percent confidence level and 198 million barrels at a 50 percent confidence level. Nonetheless, limited research was available or published to further define the play and the reservoir characteristics. The specific objectives of the enclosed research were to (1) characterize geologic, geochemical, and geomechanical rock properties of target zones in the two designated basins by compiling data and by analyzing available cores, cuttings, and well logs; (2) describe outcrop reservoir analogs of GRF plays (Cane Creek shale is not exposed) and compare them to subsurface data; (3) map major regional trends for targeted intervals and identify “sweet spots” that have the greatest oil potential; (4) reduce exploration costs and drilling risks, especially in environmentally sensitive areas; (5) improve drilling and fracturing effectiveness by determining optimal well completion design; and (6) reduce field development costs, maximize oil recovery, and increase reserves. These objectives are all addressed in a series of nine publications that resulted from this extensive research project. Each publication is included in this report as an independent appendix.

02 PETROLEUM↗
DOE OSTI2022

Redox oxide@molten salt as a generalized catalyst design strategy for oxidative dehydrogenation of ethane via selective hydrogen combustion

Here, the current study demonstrates a redox oxide @ molten salt core-shell architecture as a generalized redox catalyst design strategy for chemical looping – oxidative dehydrogenation of ethane. 17 combinations of redox active oxides and molten salts were prepared, evaluated, and characterized. X-ray diffraction indicates that the redox oxides and molten salts are fully compatible, forming separate and stable phases. X-ray photoelectron spectroscopy demonstrates that the molten salts aggregate at the redox oxide surface, forming a core-shell structure to block the non-selective sites responsible for CO x formation. Up to ~74% single-pass olefin yields were achieved using the proposed redox catalyst design strategy. Statistical analyses of the performance data indicate the potential to achieve up to 86.7% single-pass yield by simply optimizing the operating conditions using the redox catalysts reported in this study. Meanwhile, the generalizability of the catalyst design strategy offers exciting opportunities to further optimize the composition and performance of the redox catalysts for ethane ODH under a chemical looping scheme with significantly reduced energy consumption and CO 2 emissions.

03 NATURAL GAS↗
DOE OSTI2017

Structural Evolution of Sub-10 nm Octahedral Platinum$-$Nickel Bimetallic Nanocrystals

Octahedral Pt alloy nanocrystals (NCs) have shown excellent activities as electrocatalysts toward oxygen reduction reaction (ORR). As the activity and stability of NCs are highly dependent on their structure and the elemental distribution, it is of great importance to understand the formation mechanism of octahedral NCs and to rationally synthesize shape-controlled alloy catalysts with optimized ORR activity and stability. However, the factors controlling the structural and compositional evolution during the synthesis have not been well understood yet. Here in this paper, we systematically investigated the structure and composition evolution pathways of Pt–Ni octahedra synthesized with the assistance of W(CO) 6 and revealed a unique core–shell structure consisting of a Pt core and a Pt–Ni alloy shell. Below 140 °C, sphere-like pure Pt NCs with the diameter of 3–4 nm first nucleated, followed by the isotropic growth of Pt–Ni alloy on the seeds at temperatures between 170 and 230 °C forming Pt@Pt–Ni core–shell octahedra with {111} facets. Owing to its unique structure, the Pt@Pt–Ni octahedra show an unparalleled stability during potential cycling, that is, no activity drop after 10 000 cycles between 0.6 and 1.0 V. This work proposes the Pt@Pt–Ni octahedra as a high profile electrocatalyst for ORR and reveals the structural and composition evolution pathways of Pt-based bimetallic NCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Heat loads to divertor nearby components from secondary radiation evolved during plasma instabilities

A fundamental issue in tokamak operation related to power exhaust during plasma instabilities is the understanding of heat and particle transport from the core plasma into the scrape-off layer and to plasma-facing materials. During abnormal and disruptive operation in tokamaks, radiation transport processes play a critical role in divertor/edge-generated plasma dynamics and are very important in determining overall lifetimes of the divertor and nearby components. This is equivalent to or greater than the effect of the direct impact of escaped core plasma on the divertor plate. We have developed and implemented comprehensive enhanced physical and numerical models in the upgraded HEIGHTS package for simulating detailed photon and particle transport in the evolved edge plasma during various instabilities. The paper describes details of a newly developed 3D Monte Carlo radiation transport model, including optimization methods of generated plasma opacities in the full range of expected photon spectra. Response of the ITER divertor's nearby surfaces due to radiation from the divertor-developed plasma was simulated by using actual full 3D reactor design and magnetic configurations. We analyzed in detail the radiation emission spectra and compared the emission of both carbon and tungsten as divertor plate materials. The integrated 3D simulation predicted unexpectedly high damage risk to the open stainless steel legs of the dome structure in the current ITER design from the intense radiation during a disruption on the tungsten divertor plate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Design and optimization of nanoparticle-pigmented solar selective absorber coatings for high-temperature concentrating solar thermal systems

In this paper, we present a systematic approach for the design and optimization of nanoparticle-pigmented solar selective absorbers for operation at 750 °C. Using the scattering and absorption cross-sections calculated by Lorenz-Mie scattering theory as input, we employ a four-flux radiative transfer method to investigate the solar selectivity mechanism and optimize the optical-to-thermal conversion efficiency (η therm ) as a function of the metallic nanoparticle material, the nanoparticle diameter, the volume fraction, and the coating thickness. Among the nanoparticle material candidates in this study, C54-TiSi 2 is the best option with an optimized η therm = 87.0% for a solar concentration ratio of C = 100 and η therm = 94.4% for C = 1000 at 750 °C. NiSi is also a promising candidate comparable to TiSi 2 in thermal efficiency, Experimentally, an un-optimized 200 nm-diameter TiSi 2 nanoparticle-silicone solar selective coating has already achieved η therm = 89.8% for C = 1000 at 750 °C. This performance is consistent with the theoretical model and close to the thermal efficiency of the commercial Pyromark 2500 coatings (90.1%). We also demonstrate that Ni/NiSi core-shell structures embedded in the SiO 1.5 matrix is thermally stable at 750 °C for 1000 h in air. Lastly, these results indicate that silicide cermet coatings are promising to achieve high optical performance and high temperature thermal stability simultaneously.

14 SOLAR ENERGY↗
DOE OSTI2018

Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction

We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, transfers and computations, high efficiency with respect to single GPU kernel performance is maintained up to the full Titan and Summit systems. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.

Joubert, Wayne↗
DOE OSTI2022

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Furthermore, our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

High Performance Data Transfer for Distributed Data Intensive Sciences

We report on the development of ZX software providing high performance data transfer and encryption. The design scales in: computation power, network interfaces, and IOPS while carefully balancing the available resources. Two U.S. patent-pending algorithms help tackle data sets containing lots of small files and very large files, and provide insensitivity to network latency. It has a cluster-oriented architecture, using peer-to-peer technologies to ease deployment, operation, usage, and resource discovery. Its unique optimizations enable effective use of flash memory. Using a pair of existing data transfer nodes at SLAC and NERSC, we compared its performance to that of bbcp and GridFTP and determined that they were comparable. With a proof of concept created using two four-node clusters with multiple distributed multi-core CPUs, network interfaces and flash memory, we achieved 155Gbps memory-to-memory over a 2x100Gbps link aggregated channel and 70Gbps file-to-file with encryption over a 5000 mile 100Gbps link.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Validation of an integrated modeling framework for investigating 3D plasma responses in tokamak plasmas

As contemporary experimental tokamaks are pushed toward reactor-relevant operation, they provide essential testbeds for demonstrating and exploring ELM control strategies for deployment in future fusion pilot plants. Accurate predictions of full plasma responses are essential to guide and optimize these demonstrations. This paper introduces and validates an integrated modeling framework over a historical range of DIII-D operational space. The integrated modeling framework uses only scalar plasma parameters and optional reference boundary to self-consistently and flexibly scan through tokamak operational space and estimate the corresponding ELM-suppression relevant plasma response over 3D coil phase space. The framework generates tightly converged equilibria that satisfy a target set of plasma parameters (I p , β N , l i ), with kinetic profiles constrained by an EPED(NN)-computed pedestal and empirical core model. The plasma response of these modeled equilibria is calculated with GPEC. Validated against 55 distinct DIII-D equilibria, the framework consistently reproduces experimentally constrained equilibrium pressure, q, and other representative profiles using the time-varying information from only evolving scalar plasma parameters; plasma response validation is performed for the n=3 perturbation. In particular, the plasma response of a DIII-D discharge scanning q 95 in search of ELM suppression windows is quantitatively reproduced. By accurately reproducing experimental equilibria and plasma responses across wide parameter variation, the framework supports potential for synthetic parameter scans in key operational, stability, and plasma response dimensions to investigate RMP ELM-suppression experiments and inform predictive RMP scenario optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.

42 ENGINEERING↗
DOE OSTI2016

Integrated fusion simulation with self-consistent core-pedestal coupling

In this study, accurate prediction of fusion performance in present and future tokamaks requires taking into account the strong interplay between core transport, pedestal structure, current profile and plasma equilibrium. An integrated modeling workflow capable of calculating the steady-state self- consistent solution to this strongly-coupled problem has been developed. The workflow leverages state-of-the-art components for collisional and turbulent core transport, equilibrium and pedestal stability. Validation against DIII-D discharges shows that the workflow is capable of robustly pre- dicting the kinetic profiles (electron and ion temperature and electron density) from the axis to the separatrix in good agreement with the experiments. An example application is presented, showing self-consistent optimization for the fusion performance of the 15 MA D-T ITER baseline scenario as functions of the pedestal density and ion effective charge Z eff .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Designing a parallel Feel-the-Way clustering algorithm on HPC systems

This paper introduces a new parallel clustering algorithm, named Feel-the-Way clustering algorithm, that provides better or equivalent convergence rate than the traditional clustering methods by optimizing the synchronization and communication costs. Our algorithm design centers on how to optimize three factors simultaneously: reduced synchronizations, improved convergence rate, and retained same or comparable optimization cost. To compare the optimization cost, we use the Sum of Square Error (SSE) cost as the metric, which is the sum of the square distance between each data point and its assigned clusters. Compared with the traditional MPI k-means algorithm, the new Feel-the-Way algorithm requires less communications among participating processes. As for the convergence rate, the new algorithm requires fewer number of iterations to converge. As for the optimization cost, it obtains the SSE costs that are close to the k-means algorithm. In the paper, we first design the full-step Feel-the-Way k-means clustering algorithm that can significantly reduce the number of iterations that are required by the original k-means clustering method. Next, we improve the performance of the full-step algorithm by adopting an optimized sampling-based approach, named reassignment-history-aware sampling. Our experimental results show that the optimized sampling-based Feel-the-Way method is significantly faster than the widely used k-means clustering method, and can provide comparable optimization costs. More extensive experiments with several synthetic datasets and real-world datasets (e.g., MNIST, CIFAR-10, ENRON, and PLACES-2) show that the new parallel algorithm can outperform the open source MPI k-means library by up to 110% on a high-performance computing system using 4,096 CPU cores. In addition, the new algorithm can take up to 51% fewer iterations to converge than the k-means clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Rendering and Compositing Infrastructure Improvements to VisIt for Insitu Rendering

Compared to posthoc rendering, insitu rendering often generates larger numbers of images, as a result rendering performance and scalability are critical in the insitu setting. In this work we present improvements to VisIt's rendering and compositing infrastructure that deliver increased performance and scalability in both posthoc and insitu settings. We added the capability for alpha blend compositing and use it with ordered compositing when datasets have disjoint block domain decomposition to optimize the rendering of transparent geometry. We also made improvements that increase overall efficiency by reducing communication and data movement and have addressed a number of performance issues. We structured our code to take advantage of SIMD parallelization and use threads to overlap communication and compositing. We tested our improvements on a 20 core workstation using 8 cores to render geometry generated from a $256^3$ cosmology dataset and on a Cray XC31 using 512 cores to render geometry generated from a $2000^2 \times 800$ plasma dataset. Our results show that ordered compositing provides a speed up of up to $4 \times$ over the current sort first strategy. The other improvements resulted in modest speed up with one notable exception where we achieve up to $40 \times$ speed up of rendering and compositing of opaque geometry when both opaque and transparent geometry are rendered together. We also investigated the use of depth peeling, but found that the implementation provided by VTK is substantially slower,both with and without GPU acceleration, than a local camera order sort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

RAFT: Reconfigurable Array of High-Efficiency Ducted Turbines for Hydrokinetic Energy Harvesting

Diversifying the energy harvesting portfolio is crucial to achieving the ambitious goal of transitioning to clean energy by 2030. Marine hydrokinetic energy has garnered renewed interest due to its high harvesting potential in the U.S., and the resource's reliability and predictability—remaining relatively constant on a daily basis and available 24/7. However, there are currently few commercial devices capable of harnessing the energy from flowing water. This project aims to bridge that gap by designing and evaluating a novel hydrokinetic turbine concept that can efficiently harvest energy from both rivers and tidal streams. The RAFT (Reconfigurable Array of High-Efficiency Ducted Turbines) concept introduces a duct surrounding the turbine rotor and creates an array of small 5-kW units. The duct serves two primary purposes: (1) it enhances hydrodynamic efficiency by accelerating flow to the rotor, and (2) it functions as a structural component, facilitating the formation of modular arrays that lower costs. This project focuses on demonstrating this concept and validating these benefits through simulations and scaled prototype testing. The project team includes 8 faculty members and over 20 students from 3 universities, organized into three core areas: hydrodynamics, electrical systems, and structural analysis, with additional teams dedicated to system integration, environmental assessment and risk management, and tech-to-market strategy. The team successfully demonstrated the increased hydrodynamic efficiency of a ducted turbine compared to an unducted version using high-fidelity simulations and prototype tests. Moreover, design optimization efforts led to surpassing the SHARKS program's goal of 60% reduction of the levelized cost of energy with a significant margin.

13 HYDRO ENERGY↗
DOE OSTI2019

Synchrotron Radiation Research and Analysis of the Particulate Matter in Deep Ice Cores: An Overview of the Technical Challenges

Airborne dust extracted from deep ice core perforations can provide chemical and mineralogical insight into the history of the climate and atmospheric conditions, with unrivalled temporal resolution, time span and richness of information. The availability of material for research and the natural complexity of the particulate, however, pose significant challenges to analytical methods. We present the developments undertaken to optimize the experimental techniques, materials and protocols for synchrotron radiation-based analysis, in particular for the acquisition of combined Synchrotron Radiation X-Ray Fluorescence and X-ray Absorption Spectroscopy data.

Cibin, Giannantonio↗
DOE OSTI2017

A Methodology for Dynamic Security Risk Quantification and Optimal Resource Allocation of Security Assets

In this presentation we overview a methodology for dynamic security risk quantification and optimal resource allocation of security assets for high profile venues. This methodology is especially applicable to venues that require security screening operations such as mass transit (e.g., train or airport terminals), critical infrastructure protection (e.g., government buildings), and largescale public events (e.g., concerts or professional sports). The method starts by decomposing the three core components of risk -- threat, vulnerability, and consequence -- into their various subcomponents. For instance, vulnerability can be decomposed into availability, accessibility, organic security, and target hardness and each of these can be evaluated against the potential threats of interest for the given venue. Once evaluated, these subcomponents are rolled back up to compute the specific value for the vulnerability core risk component. Likewise, the same is done for consequence and threat, and then risk is computed as the product of these three components. A key aspect of our methodology is dynamically quantifying risk. That is, we incorporate the ability to uniquely allow the subcomponents and core components, and in turn, risk, to be quantified as a continuous function of time throughout the day, week, month, or year as appropriate.

Brigantic, Robert T.↗