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X-composer: enabling cross-environments in-situ workflows between HPC and cloud

As large-scale scientific simulations and big data analyses become more popular, it is increasingly more expensive to store huge amounts of raw simulation results to perform post-analysis. To minimize the expensive data I/O, "in-situ" analysis is a promising approach, where data analysis applications analyze the simulation generated data on the fly without storing it first. However, it is challenging to organize, transform, and transport data at scales between two semantically different ecosystems due to the distinct software and hardware difference. To tackle these challenges, we design and implement the X-Composer framework. X-Composer connects cross-ecosystem applications to form an "in-situ" scientific workflow, and provides a unified approach and recipe for supporting such hybrid in-situ workflows on distributed heterogeneous resources. X-Composer reorganizes simulation data as continuous data streams and feeds them seamlessly into the Cloud-based stream processing services to minimize I/O overheads. For evaluation, we use X-Composer to set up and execute a cross-ecosystem workflow, which consists of a parallel Computational Fluid Dynamics simulation running on HPC, and a distributed Dynamic Mode Decomposition analysis application running on Cloud. Our experimental results show that X-Composer can seamlessly couple HPC and Big Data jobs in their own native environments, achieve good scalability, and provide high-fidelity analytics for ongoing simulations in real-time.

Wang, Dali↗

Tabletop Testing for EV Charging Ecosystem PKI (Project T34PKI Final Report)

To test the communications and cybersecurity functionality, Electric Vehicle and charging station vendors have had to ship their products to in-person testing events. This is cumbersome, expensive, inefficient, and an impediment to rapid time-to-deployment. In this project Sandia used COTS hardware and Open-Source Software to develop and demonstrate a more agile, productive approach: testing low-voltage controllers independently from high-voltage power delivery sub-systems. This approach allows communications controllers to be transported easily (e.g. shipped at low cost, checked as airline baggage); set up on a table-top (“bench testing”); and use ordinary 120 VAC outlets to conduct agile testing. Table-top platforms become end nodes that can connect to laboratory and cloud-based servers to test communications and cybersecurity, specifically Public Key Infrastructure (PKI) functionality and interoperability, separately from EV battery charging (power/energy transfer) functionality.

33 ADVANCED PROPULSION SYSTEMS↗

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

54 ENVIRONMENTAL SCIENCES↗

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗

BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyber-infrastructures which enable effcient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing{structure{property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing{structure{property relationships, can be implemented into the system. Speci cally, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally effcient data-driven model into the BisQue platform. The new module is made available online and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

36 MATERIALS SCIENCE↗

TU-CD-304-11: Veritas 2.0: A Cloud-Based Tool to Facilitate Research and Innovation

Purpose: We introduce Veritas 2.0, a cloud-based, non-clinical research portal, to facilitate translation of radiotherapy research ideas to new delivery techniques. The ecosystem of research tools includes web apps for a research beam builder for TrueBeam Developer Mode, an image reader for compressed and uncompressed XIM files, and a trajectory log file based QA/beam delivery analyzer. Methods: The research beam builder can generate TrueBeam readable XML file either from scratch or from pre-existing DICOM-RT plans. DICOM-RT plan is first converted to XML format and then researcher can interactively modify or add control points to them. Delivered beam can be verified via reading generated images and analyzing trajectory log files. Image reader can read both uncompressed and HND-compressed XIM images. The trajectory log analyzer lets researchers plot expected vs. actual values and deviations among 30 mechanical axes. The analyzer gives an animated view of MLC patterns for the beam delivery. Veritas 2.0 is freely available and its advantages versus standalone software are i) No software installation or maintenance needed, ii) easy accessibility across all devices iii) seamless upgrades and iv) OS independence. Veritas is written using open-source tools like twitter bootstrap, jQuery, flask, and Python-based modules. Results: In the first experiment, an anonymized 7-beam DICOM-RT IMRT plan was converted to XML beam containing 1400 control points. kV and MV imaging points were inserted into this XML beam. In another experiment, a binary log file was analyzed to compare actual vs expected values and deviations among axes. Conclusions: Veritas 2.0 is a public cloud-based web app that hosts a pool of research tools for facilitating research from conceptualization to verification. It is aimed at providing a platform for facilitating research and collaboration. I am full time employee at Varian Medical systems, Palo Alto.

60 APPLIED LIFE SCIENCES↗

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Metabolomic data streaming for biology-dependent data acquisition

During the last 10 years, metabolomics has emerged as a powerful technology to interrogate cellular biochemistry at the global level. Although much of the success has been driven by advances in mass spectrometry, developments in bioinformatic resources for data processing have been equally important. Additionally, the widely used metabolomic software XCMS, in particular, has undergone substantial improvements since its first introduction in 2005 (ref. 1). In addition to improved algorithms for peak picking, retention time alignment and data visualization, XCMS has transitioned from a command-line interface requiring expertise with the R programming language, to a web-based platform with a graphical user interface2. This web-based platform, which we call XCMS Online, enables thousands of users to upload their metabolomic data and perform cloud-based processing.

59 BASIC BIOLOGICAL SCIENCES↗

Dynamic Provisioning and Execution of HPC Workflows Using Python

High-performance computing (HPC) workflows over the last several decades have proven to assist in the understanding of scientific phenomena and the production of better products, more quickly, and at reduced cost. However, HPC workflows are difficult to implement and use for a variety of reasons. In this paper, we describe the development of the Python-based cumulus, which addresses many of these barriers. cumulus is a platform for the dynamic provisioning and execution of HPC workflows. cumulus provides the infrastructure needed to build applications that leverage traditional or Cloud-based HPC resources in their workflows. Lastly, we demonstrate the use of cumulus in both web and desktop simulation applications, as well as in an Apache Spark-based analysis application.

97 MATHEMATICS AND COMPUTING↗

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗

Foldy: An open-source web application for interactive protein structure analysis

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

59 BASIC BIOLOGICAL SCIENCES↗

Enabling a systems biology knowledgebase with gaggle and firegoose

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is an open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.

97 MATHEMATICS AND COMPUTING↗

Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures

This report summarizes the final product developed for the US DOE Small Business Innovation Research (SBIR) Phase II grant made to Engineering Mechanics Corporation of Columbus (Emc 2 ) between April 16, 2014 and August 31, 2016 titled ‘Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures’. Many US companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more cost-efficient overall with higher quality. One significant advantage that has emerged in the US over the last two decades is the use of virtual design for fabrication of small and large structures in weld fabrication industries. Industries that use virtual design and analysis tools have reduced material part size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Emc 2 ’s DOE SBIR Phase II final results to extend an existing, state-of-the-art software code, Virtual Fabrication Technology (VFT®), currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT® helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT® uses material properties, consumable properties, etc. as inputs. Through VFT®, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. Finally service performance (such as fatigue, corrosion, and fracture/damage) can be improved using this product. Emc 2 ’s DOE SBIR Phase II effort successfully adapted VFT® to perform efficiently in an HPC environment independent of commercial software on a platform to permit easy and cost effective access to the code. This provides the key for SMEs to access this sophisticated and proven methodology that is quick, accurate, cost effective and available “on-demand” to address weld-simulation and fabrication problems prior to manufacture. In addition, other organizations, such as Government agencies and large companies, may have a need for spot use of such a tool. The open source code, WARP3D, a high performance finite element code used in fracture and damage assessment of structures, was significantly modified so computational weld problems can be solved efficiently on multiple processors and threads with VFT®. The thermal solver for VFT®, based on a series of closed form solution approximations, was extensively enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) was re-written to permit SMEs access to an HPC environment at the Ohio Super Computer Center (OSC) to integrate these solutions with WARP3D. The GUI is used to define all weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was enhanced to make it more user-friendly so that non-experts can perform weld modeling. Finally, an extensive outreach program to market this capability to fabrication companies was performed. This access will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaluation of Data Catalog Software for Hanford Site Environmental Datasets

Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.

54 ENVIRONMENTAL SCIENCES↗

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology

This whitepaper provides strategic insights and recommendations into security cloud-based solutions for electric utilities, encompassing operational technology (OT), virtual power plants (VPP), distributed energy resources (DERs), applications, networks, and data storage as they transition to and leverage cloud infrastructure through managed service providers (MSPs) and cloud service providers (CSPs). Principles derived from established frameworks serve as a foundation for best practices across cybersecurity projects and remove the constraints of settling on a single framework. For organizations that prefer not to integrate a specific framework altogether, elements of the proposed approach could be adopted or tailored to best fit defined requirements and expected functionalities. The Cirrus assessment, a utility cloud feasibility tool, and the roadmap it provides serve as a precursor to this paper, which seeks to be a valuable resource for defining next steps following cloud technology integration feasibility appraisal. With its comprehensive approach to adoption, the Cirrus framework offers strategic guidance on responsibly preparing for or deploying a utility cloud solution. The previously published whitepaper, “Use Case-Informed Framework for Utility Cloud Migration,” details the guiding strategy, research, and deployment of cloud solutions within electric and interconnected grid systems. Before implementing the controls suggested in this document, it is recommended that stakeholders complete Cirrus's cloud integration assessment and pair the results with their unique cybersecurity controls to form a comprehensive cloud-based utility cybersecurity plan. The Cirrus outcome will consider a series of future architectures for the grid before and after the energy transition and evaluate the arguments for and against cloud applications for each electric and interconnected grid layer. This document is a companion to the original whitepaper, "Use Case-Informed Framework for Utility Cloud Migration" to further identify and recommend security controls based on Cirrus’s cloud integration assessment output. The following whitepaper outlines the cybersecurity controls that secure cloud-service models pertinent to the electric sector using the predefined categories identify, protect, detect, and respond and recover. The objective is to outline prescriptive security controls based on the type of architecture and data stored in the cloud. The focus includes dissecting the shared responsibility model and elucidating what on-premises Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) entail. A pivotal consideration in this context is allocating responsibility for foundational cybersecurity aspects—having used Cirrus for the cloud integration assessment. The ensuing controls detailed herein also represent a checklist of controls necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and strategic foresight in a safe and responsible manner.

42 ENGINEERING↗

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗