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DOE OSTI2015

Performance analysis of data intensive cloud systems based on data management and replication: a survey

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Malik, Saif Ur Rehman↗

DOE OSTI2014

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD) based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Projection Microstereolithography for Architected Materials

Stereolithography (SL) is a prominent paradigm in the realm of additive manufacturing (AM) technologies, which refers to fabrication approaches that build “bottom up” by adding material. Pioneered in 1984 by Chuck Hull, SL relies on light-activated solidification of a liquid photosensitive resin in a layer-by-layer fashion, with the geometry of each layer defined by sectioning a three-dimensional (3D) computer model by closely-spaced parallel planes. The maturation of the technology over nearly three subsequent decades has resulted in the wide availability of commercial SL products, such as the ProJet series from 3D Systems, Inc. (which uses the proprietary acronym SLA to refer to SL technology), which routinely produces parts with 50-100 µm resolution in all three axes, and overall dimensions of 0.25-0.5 m. In the most general terms, advancements in SL technology have generally been aimed at achieving four goals: decreasing (1) system cost and (2) minimum feature size (i.e. improving resolution), while (3) increasing overall part size and (4) build speed. SL systems can be broadly categorized into two classes, by the way in which they deliver illumination to produce the shape of each object layer to be fabricated: laser-scanning and mask projection approaches. Commercially-available SL systems incorporate the laser-scanning approach, following the original scheme described by Hull, since digital image projection technology has only emerged relatively recently as a viable alternative. A significant limitation of laser scanning is the relatively slow speed of rastering the laser to form each layer’s pattern, highlighting the need for a dynamic mask-based approach to polymerize larger areas in a single exposure. The recent proliferation of digital display technologies (referred to as spatial light modulators or SLMs) has enabled the implementation of such “dynamic masking,” increasing system throughput by allowing a complete image layer to be cross-linked simultaneously. These mask projection SL systems are gaining greater prominence, and the landscape of some of the parameters available for system design, as implemented in recent work by a variety of investigators, has been reviewed by Lambert et al.

36 MATERIALS SCIENCE↗

DOE OSTI2016

SU-F-J-183: Interior Region-Of-Interest Tomography by Using Inverse Geometry System

Purpose: The inverse geometry computed tomography (IGCT) composed of multiple source and small size detector has several merits such as reduction of scatter effect and large volumetric imaging within one rotation without cone-beam artifact, compared to conventional cone-beam computed tomography (CBCT). By using this multi-source characteristics, we intend to present a selective and multiple interior region-of-interest (ROI) imaging method by using a designed source on-off sequence of IGCT. Methods: All of the IGCT sources are operated one by one sequentially, and each projection in the shape of narrow cone-beam covers its own partial volume of full field of view (FOV) determined from system geometry. Thus, through controlling multi source operation, limited irradiation within ROI is possible and selective radon space data for ROI imaging can be acquired without additional X-ray filtration. With this feature, we designed a source on-off sequence for multi ROI-IGCT imaging, and projections of ROI-IGCT were generated by using the on-off sequence. Multi ROI-IGCT images were reconstructed by using filtered back-projection algorithm. All these imaging process of our study has been performed by utilizing digital phantom and patient CT data. ROI-IGCT images of the phantom were compared to CBCT image and the phantom data for the image quality evaluation. Results: Image quality of ROI-IGCT was comparable to that of CBCT. However, the distal axial-plane from the FOV center, large cone-angle region, ROI-IGCT showed uniform image quality without significant cone-beam artifact contrary to CBCT. Conclusion: ROI-IGCT showed comparable image quality and has the capability to provide multi ROI image within a rotation. Projection of ROI-IGCT is performed by selective irradiation, hence unnecessary imaging dose to non-interest region can be reduced. In this regard, it seems to be useful for diagnostic or image guidance purpose in radiotherapy such as low dose target localization and patient alignment. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Schema Elements for Granta Annual Report

Granta: Materials Intelligence, also known as Granta:MI or Granta, is a commercial database software by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store materials data relevant to the complex. The software was first adopted by individual sites in the early 2000’s, but in FY20, enterprise-wide licensing was purchased for the NSE by the Product Realization Integrated Digital Enterprise (PRIDE) program. Currently, there are 375 floating licenses available for users at all sites. There are also two shared instances of Granta; a classified production instance on the Enterprise Secure Network (ESN) and an unclassified development instance. Additionally, some individual sites such as Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL), Kansas City National Security Campus (KCNSC), and Savannah River National Laboratory (SRNL) host their own local Granta instances on their own networks that, in general, only their employees can access.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Quantum Lock Technologies: Innovation Crossroads Final Report

At Quantum Lock Technologies, our mission is to use future-proof hardware and software to bridge the gap between physical access control and cyber security. Physical security includes access to doors, lockboxes/containers, and machinery/robots. Connecting physical access control to the cloud allows for remote detection, fast ledger updates, and mobile or remote access. However, this also opens physical security up to the world of cyber-attacks. At Quantum Lock, we use quantum random number generation to generate completely random and unpredictable digital keys to be used by connected equipment in a facility. This quantum technology is then paired with end-to-end encryption and a one-time-key communication protocol to ensure the highest level of security. Through the Innovation Crossroads program at Oak Ridge National Laboratory, we have developed benchtop prototypes of our technology, connected with utility boards as our first target customers, and prepared for our first pilot with customers (target end of summer 2022). Below is a photograph of myself at an energy substation where we plan to eventually apply our technology.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

RIA simulation tests using driver tube for ATF cladding

Pellet-cladding mechanical interaction (PCMI) is a potential failure mechanism for accident-tolerant fuel (ATF) cladding candidates during a reactivity-initiated accident (RIA). This report summarizes Fiscal Year (FY) 2017 research activities that were undertaken to evaluate the PCMI-like hoop-strain-driven mechanical response of ATF cladding candidates. To achieve various RIA-like conditions, a modified-burst test (MBT) device was developed to produce different mechanical pulses. The calibration of the MBT instrument was accomplished by performing mechanical tests on unirradiated Generation-I iron-chromium-aluminum (FeCrAl) alloy samples. Shakedown tests were also conducted in both FY 2016 and FY 2017 using unirradiated hydrided ZIRLO™ tube samples. This milestone report focuses on testing of ATF materials, but the benchmark tests with hydrided ZIRLO™ tube samples are documented in a recent journal article.a For the calibration and benchmark tests, the hoop strain was monitored using strain gauges attached to the sample surface in the hoop direction. A novel digital image correlation (DIC) system composed of a single high-speed camera and an array of six mirrors was developed for the MBT instrument to better resolve the failure behavior of samples and to provide useful data for validation of high-fidelity modeling and simulation tools. The DIC system enable a 360° view of a sample’s outer surface. This feature was added to the instrument to determine the precise failure location on a sample’s surface for strain predictions. The DIC system was tested on several silicon carbide fiber/silicon carbide matrix (SiC/SiC) composite tube samples at various pressurization rates of the driver tube (which correspond to the strain rates for the samples). The hoop strains for various loading conditions were determined for the SiC/SiC composite tube samples. Future work is planned to enhance understanding of the failure behavior of the ATF cladding candidates of age-hardened FeCrAl alloys and SiC/SiC composites in detail during RIA conditions informed by the computational studies performed under the US Department of Energy Office of Nuclear Energy Advanced Fuels Campaign. The testing instrument and the new DIC system will be further developed to reach different stress-state conditions and to perform tests at elevated temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Performance characterization of high quantum efficiency metal package photomultiplier tubes for time-of-flight and high-resolution PET applications

Purpose: Metal package photomultiplier tubes (PMTs) with a metal channel dynode structure have several advanced features for devising such time-of-flight (TOF) and high spatial resolution positron emission tomography (PET) detectors, thanks to their high packing density, large effective area ratio, fast time response, and position encoding capability. Here, we report on an investigation of new metal package PMTs with high quantum efficiency (QE) for high-resolution PET and TOF PET detector modules. Methods: The latest metal package PMT, the Hamamatsu R11265 series, is served with two kinds of photocathodes that have higher quantum efficiency than normal bialkali (typical QE ≈ 25%), super bialkali (SBA; QE ≈ 35%), and ultra bialkali (UBA; QE ≈ 43%). In this study, the authors evaluated the performance of the new PMTs with SBA and UBA photocathodes as a PET detector by coupling various crystal arrays. They also investigated the performance improvements of high QE, focusing in particular on a block detector coupled with a lutetium-based scintillator. A single 4 × 4 × 10 mm{sup 3} LYSO, a 7 × 7 array of 3 × 3 × 20 mm{sup 3} LGSO, a 9 × 9 array of 1.2 × 1.2 × 10 mm{sup 3} LYSO, and a 6 × 6 array of 1.5 × 1.5 × 7 mm{sup 3} LuYAP were used for evaluation. All coincidence data were acquired with a DRS4 based fast digitizer. Results: This new PMT shows promising crystal positioning accuracy, energy and time discrimination performance for TOF, and high-resolution PET applications. The authors also found that a metal channel PMT with SBA was enough for both TOF and high-resolution application, although UBA gave a minor improvement to time resolution. However, significant performance improvement was observed in relative low light output crystals (LuYAP) coupled with UBA. Conclusions: The results of this study will be of value as a useful reference to select PMTs for high-performance PET detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Data Readiness for AI: A 360-Degree Survey

Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Hydropower Cybersecurity Value-at-Risk Framework

Hydropower remains one of the strongest forms of renewable energy generation methods. It is crucial to address the increasing risks associated with the rapid digitization. The push towards decarbonization also factors in the need to ensure security and resilience for grid-connected renewable energy resources. This report summarizes the U.S. Department of Energy's Water Power Technologies Office's effort to develop a cybersecurity valuation methodology that assists hydropower stakeholders in assessing risks associated with plan operations and gathers valuation guidance through a web-based application. The Hydropower Cybersecurity Value-at-Risk Framework delivers a platform for industry members to perform self-assessments and make informed decisions on their cybersecurity investments.

13 HYDRO ENERGY↗

DOE OSTI2022

Status Report on Regulatory Criteria Applicable to the Use of Digital Twins

This report describes the research results to date to identify regulatory implications of DT technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of predictive maintenance DTs, artificial intelligence (AI), and automation. The focus of this review included determination of constraints on the application of DT technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. The research included review of topical reports and other submissions to the US Nuclear Regulatory Commission (NRC) on technologies relevant to using DTs for predictive maintenance, NRC safety evaluation (SE) reports, and other relevant literature to identify specific regulatory concerns.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Understanding the Elastic, Plastic, and Damage Features in Fracturing of Self-reinforced Thermoplastic Composites via Non-destructive Digital Imaging Correlation

This work demonstrated the utilization of non-destructive Digital Imaging Correlation (DIC) method to characterize the elastic, plastic, and damage features during the Mode I intra-laminar fracturing process of self-reinforced thermoplastic composites by using a self-reinforced polypropylene (PP) composite as an example. The DIC results clearly showed the development of huge plastic zone (PZ) and non-negligible Fracture Process Zone (FPZ) in front of the notch tip during the fracturing process, and the geometries and sizes of the foregoing zones at the peak load were further quantified. Such an interesting fracturing behavior of self-reinforced thermoplastic composites is way different from brittle materials (e.g., glass, acrylic, etc.), ductile materials (e.g., aluminum, steel, etc.), and even quasi-brittle materials (e.g., concrete, nanoparticle-reinforced composites, tough ceramics, wood, cement, carbon/glass fiber-reinforced polymers, etc.). Thus, understanding the elastic, plastic, and damage features is the first step before better characterizing the material fracture properties of self-reinforced thermoplastic composites through new analytical methods and computational modeling. These efforts are of utmost importance for wide applications of self-reinforced thermoplastic composites in various engineering fields in the future.

Lightweight Composites, Self-reinforced Thermoplas↗

DOE OSTI2018

Final Report: Optimal Model Complexity in Geological Carbon Sequestration: A Response Surface Uncertainty Analysis

The critical component of a risk assessment study in evaluating GCS is an analysis of uncertainty in CO2 modeling. In such analyses, direct numerical simulation of CO2 flow and leakage requires many time-consuming model runs. Alternatively, analytical methods have been developed which allow fast and efficient estimation of CO2 storage and leakage, although restrictive assumptions on formation rock and fluid properties are employed. In this study, an intermediate approach is proposed based on the Design of Experiment and Response Surface methodology, which consists of using a limited number of numerical simulations to estimate a prediction outcome as a combination of the most influential uncertain site properties. The methodology can be implemented within a Monte Carlo framework to efficiently assess parameter and prediction uncertainty while honoring the accuracy of numerical simulations. The choice of the uncertain properties is flexible and can include geologic parameters that influence reservoir heterogeneity, engineering parameters that influence gas trapping and migration, and reactive parameters that influence the extent of fluid/rock reactions. The method was tested and verified on modeling long-term CO2 flow, non-isothermal heat transport, and CO2 dissolution storage by coupling two-phase flow with explicit miscibility calculation using an accurate equation of state that gives rise to convective mixing of formation brine variably saturated with CO2. All simulations were performed using three-dimensional high-resolution models including a target deep saline aquifer, overlying caprock, and a shallow aquifer. To evaluate the uncertainty in representing reservoir permeability, sediment hierarchy of a heterogeneous digital stratigraphy was mapped to create multiple irregularly shape stratigraphic models of decreasing geologic resolutions: heterogeneous (reference), lithofacies, depositional environment, and a (homogeneous) geologic formation. To ensure model equivalency, all the stratigraphic models were successfully upscaled from the reference heterogeneous model for bulk flow and transport predictions (Zhang & Zhang, 2015). GCS simulation was then simulated with all models, yielding insights into the level of parameterization complexity that is needed for the accurate simulation of reservoir pore pressure, CO2 storage, leakage, footprint, and dissolution over both short (i.e., injection) and longer (monitoring) time scales. Important uncertainty parameters that impact these key performance metrics were identified for the stratigraphic models as well as for the heterogeneous model, leading to the development of reduced/simplified models at lower characterization cost that can be used for the reservoir uncertainty analysis. All the CO2 modeling was conducted using PFLOTRAN – a massively parallel, multiphase, multi-component, and reactive transport simulator developed by a multi-laboratory DOE/SciDAC (Scientific Discovery through Advanced Computing) project (Zhang et al., 2017, in review). Within the uncertainty analysis framework, increasing reservoir depth were investigated to explore its effect on the uncertainty outcomes and the potential for developing gravity-stable injection with increased storage security (Dai et al., 20126; Dai et al., 2017, in review). Finally, to accurately model CO2 fluid-rock reactions and resulting long-term storage as secondary carbonate minerals, a modified kinetic rate law for general mineral dissolution and precipitation was proposed and verified that is invariant to a scale transformation of the mineral formula weight. This new formulation will lead to more accurate assessment of mineral storage over geologic time scales (Lichtner, 2016).

58 GEOSCIENCES↗

DOE OSTI2024

High-Fidelity Neutronics Model of a Realistic Heat Pipe Microreactor Report

The creation of a digital twin requires two parallel products: a virtual model and a physical asset. This report details the development the high-fidelity reactor physics model of a realistic heat pipe micro reactor for the virtual model. The realistic heat pipe micro reactor provides a generic model that research can utilize to make safeguards considerations. The model will be leveraged for capturing diversion and misuse scenarios expected to take place in a microreactor. A python wrapper has been developed to rapidly explore various acquisition pathways by manipulating the reactor physics model on-the-fly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Quantum Lock Technologies: Innovation Crossroads Final CRADA Report

42 ENGINEERING↗

DOE OSTI2016

Quantum key management

Innovations for quantum key management harness quantum communications to form a cryptography system within a public key infrastructure framework. In example implementations, the quantum key management innovations combine quantum key distribution and a quantum identification protocol with a Merkle signature scheme (using Winternitz one-time digital signatures or other one-time digital signatures, and Merkle hash trees) to constitute a cryptography system. More generally, the quantum key management innovations combine quantum key distribution and a quantum identification protocol with a hash-based signature scheme. This provides a secure way to identify, authenticate, verify, and exchange secret cryptographic keys. Features of the quantum key management innovations further include secure enrollment of users with a registration authority, as well as credential checking and revocation with a certificate authority, where the registration authority and/or certificate authority can be part of the same system as a trusted authority for quantum key distribution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Tailoring the Properties of a Ni-Based Superalloy via Modification of the Forging Process: an ICME Approach to Fatigue Performance

Traditionally, material design and property modifications are usually associated with compositional changes. However, subtle changes in the manufacturing process parameters can also have a dramatic effect on the resulting material properties. In this work, an integrated computational materials engineering (ICME) framework is adopted to tailor the fatigue performance of a Ni-based superalloy, RR1000. An existing fatigue model is used to identify microstructural features that promote enhanced fatigue life, namely a uniform, fine grain size distribution, random orientation, a distinct grain boundary distribution (specifically high twin boundary density and limited low-angle grain boundaries). A deformation mechanism map and process models for grain boundary engineering of RR1000 are used to identify the optimal thermo-mechanical processing parameters to realize these desirable microstructural features. For validation, small-scale forgings of RR1000 were produced and heat-treated to attain fine grain and coarse grain microstructures that represent the conventionally processed and grain boundary engineered (GBE) conditions, respectively. For each of the four microstructural variants of RR1000, the twin density and grain size were characterized and were in agreement with the desired microstructural attributes. In order to validate the deformation mechanisms and fatigue behavior of the material, high-resolution digital image correlation was performed to generate strain maps relative to the microstructural features. The high density of twin boundaries was confirmed to inhibit the length of slip bands, which is directly attributed to extended fatigue life. Thus, this study demonstrated the successful role of models, both process and performance, in the design and manufacture of Ni-based superalloy disk forgings.

36 MATERIALS SCIENCE↗