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NASA NTRS2013

The Challenges of Integrating Instrumentation with Inflatable Aerodynamic Decelerators

To realize the National Aeronautics and Space Administration s (NASA) goal of landing humans on Mars, development of technologies to facilitate the landing of heavy payloads are being explored. Current entry, decent, and landing technologies are not practical when utilizing these heavy payloads due to mass and volume constraints dictated by limitations imposed by current launch vehicle fairings. Therefore, past and present technologies are now being considered to provide a mass and volume efficient solution, including Inflatable Aerodynamic Decelerators (IADs) [1]. IAD ground and flight tests are currently being conducted to develop and characterize their performance under flight-like conditions. The integrated instrumentation systems, which are key to the performance characterization in each of these tests, have proven to be a challenge compared to the instrumentation of traditional rigid aeroshells. To overcome these challenges, flexible and embedded sensing systems have been developed, along with improved instrumenting techniques. This development opportunity faces many difficult aspects specific to inflatable structures in extreme environments. These include but are not limited to: physical flexibility, packaging, temperature, structural integration and data acquisition [2]. To better define the instrumentation challenges posed by IAD technology development, a survey was conducted to identify valuable measurements for ground and flight testing. From this survey many sensing technologies were explored, resulting in a down-selection to the most viable prospects. These systems were then iterated upon in design to determine the best integration techniques specific to a 3m and 6m stacked torus IAD. Each sensing system was then integrated and employed to support the IAD testing in the National Full-Scale Aerodynamics Complex 40 x 80 wind tunnel at NASA Ames Research Center in the summer of 2012. Another challenge that has been explored is the data acquisition of IAD sensing technologies. Traditionally all space based sensing systems transmit their data through a wired interface. This limits the amount of sensors able to be integrated within the IAD due to volume and routing restrictions of the supporting signal and excitation wires. To alleviate this situation, multiple wireless data acquisition technologies have been researched and developed through rapid prototyping efforts. The final custom multi-nodal wireless system utilized during the summer 2012 IAD test series consisted of four remote nodes and one receiving base station. The system reliably conditioned and acquired 20+ sensors over the course of the wind tunnel test series. These developments in wireless data acquisition techniques can eliminate the need for structural feedthroughs and reduce system mass associated with wiring and wire harnesses. This makes the utilization of flight instrumentation more attractive to future missions, which would result in further improved characterization of IAD technology, and overall, increased scientific knowledge regarding the response of inflatable structures to extreme entry environments. [

Swanson, Gregory T.↗

DOE OSTI2020

Measurement of the integrated luminosity of the Phase 2 data of the Belle II experiment

From April to July 2018, a data sample at the peak energy of the Υ(4S) resonance was collected with the Belle II detector at the SuperKEKB electron-positron collider. This is the first data sample of the Belle II experiment. Using Bhabha and digamma events, we measure the integrated luminosity of the data sample to be (496.3±0.3±3.0)pb –1 , where the first uncertainty is statistical and the second is systematic. This work provides a basis for future luminosity measurements at Belle II.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1973

ERTS-1 - Teaching us a new way to see.

The ERTS-1 payload is discussed, giving attention to three television cameras, which view the same area in three different spectral bands. The payload includes also a multispectral scanner subsystem and a data collection system which collects information from some 150 remote, unattended, instrumented ground platforms. Many government agencies use ERTS-1 data as integral parts of their ongoing programs. Through its EROS program, the Interior Department represents the largest single recipient and user agency of data obtained from NASA aircraft and spacecraft designed to gather repetitive information related to a wide variety of earth-science and natural-resources disciplines. Questions of environmental impact are considered together with applications in agriculture, forestry, marine resources, geography, and the survey of water resources.

Mercanti, E. P.↗

NASA NTRS1981

IPAD applications to the design, analysis, and/or machining of aerospace structures

A computer software system for the processing and integration of engineering data and programs, called IPAD (Integrated Programs for Aerospace-Vehicle Design), is described. The ability of the system to relieve the engineer of the mundane task of input data preparation is demonstrated by the application of a prototype system to the design, analysis, and/or machining of three simple structures. Future work to further enhance the system's automated data handling and ability to handle larger and more varied design problems are also presented.

Blackburn, C. L.↗

DOE OSTI2013

SYSTEMATICS IN METALLICITY GRADIENT MEASUREMENTS. I. ANGULAR RESOLUTION, SIGNAL TO NOISE, AND ANNULAR BINNING

With the rapid progress in metallicity gradient studies at high redshift, it is imperative that we thoroughly understand the systematics in these measurements. This work investigates how the [N II]/H{alpha}-ratio-based metallicity gradients change with angular resolution, signal to noise (S/N), and annular binning parameters. Two approaches are used: (1) we downgrade the high angular resolution integral-field data of a gravitationally lensed galaxy and re-derive the metallicity gradients at different angular resolution; (2) we simulate high-redshift integral field spectroscopy observations under different angular resolution and S/N conditions using a local galaxy with a known gradient. We find that the measured metallicity gradient changes systematically with angular resolution and annular binning. Seeing-limited observations produce significantly flatter gradients than higher angular resolution observations. There is a critical angular resolution limit beyond which the measured metallicity gradient is substantially different to the intrinsic gradient. This critical angular resolution depends on the intrinsic gradient of the galaxy and is {<=}0.''02 for our simulated galaxy. We show that seeing-limited high-redshift metallicity gradients are likely to be strongly affected by resolution-driven gradient flattening. Annular binning with a small number of annuli produces a more flattened gradient than the intrinsic gradient due to weak line smearing. For three-annulus bins, a minimum S/N of {approx}5 on the [N II] line is required for the faintest annulus to constrain the gradients with meaningful errors.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1986

Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique.

Justice, C. O.↗

NASA NTRS1991

Techniques for containing error propagation in compression/decompression schemes

Data compression has the potential for increasing the risk of data loss. It can also cause bit error propagation, resulting in catastrophic failures. There are a number of approaches possible for containing error propagation due to data compression: (1) data retransmission; (2) data interpolation; (3) error containment; and (4) error correction. The most fruitful techniques will be ones where error containment and error correction are integrated with data compression to provide optimal performance for both. The error containment characteristics of existing compression schemes should be analyzed for their behavior under different data and error conditions. The error tolerance requirements of different data sets need to be understood, so guidelines can then be developed for matching error requirements to suitable compression algorithms.

Kobler, Ben↗

NASA NTRS1988

Robot Design

Martin Marietta Aero and Naval Systems has advanced the CAD art to a very high level at its Robotics Laboratory. One of the company's major projects is construction of a huge Field Material Handling Robot for the Army's Human Engineering Lab. Design of FMR, intended to move heavy and dangerous material such as ammunition, was a triumph in CAD Engineering. Separate computer problems modeled the robot's kinematics and dynamics, yielding such parameters as the strength of materials required for each component, the length of the arms, their degree of freedom and power of hydraulic system needed. The Robotics Lab went a step further and added data enabling computer simulation and animation of the robot's total operational capability under various loading and unloading conditions. NASA computer program (IAC), integrated Analysis Capability Engineering Database was used. Program contains a series of modules that can stand alone or be integrated with data from sensors or software tools.

Source record↗

NASA NTRS2023

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRSDate not supplied

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

Digital Transformation↗

DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗

DOE OSTI2017

Best Practice for the Commercial Deployment of Carbon Dioxide Geologic Storage: Adaptive Management Approach

The Plains CO 2 Reduction (PCOR) Partnership is one of seven regional partnerships formed as part of the U.S. Department of Energy’s National Energy Technology Laboratory Regional Carbon Sequestration Partnerships (RCSP) Initiative. The RCSP Initiative is focused on the safe and long-term storage of CO 2 to support the commercial deployment of carbon capture and storage (CCS). To that end, the PCOR Partnership has spent over 10 years developing, testing, and validating the best methods and technologies to conduct the geologic storage of CO 2 (hereafter referred to as CO 2 storage). Through this effort, the PCOR Partnership has formalized an adaptive management approach (AMA) for the commercial development of CO 2 storage projects. The use of this approach, which draws upon the collective experience and lessons learned from the PCOR Partnership, represents best practice for advancing CO 2 storage projects toward commercial deployment. At the heart of the AMA are four technical elements necessary for any successful CO 2 storage project: 1) site characterization; 2) modeling and simulation; 3) risk assessment; and 4) monitoring, verification, and accounting (MVA). Each of these elements plays a key role in gathering and assessing site-specific data that provide a fundamental understanding of the storage complex and its performance. While each of the four technical elements can provide useful data independently, integrating them through the AMA yields a streamlined, fit-for-purpose strategy for the commercial deployment of CO 2 storage. Key to this integration and resulting best practice are feedback loops that allow the results of each element to serve as inputs to the others. Each iteration of the AMA creates an improved understanding of the storage complex and thus more targeted and efficient applications of the technical elements. For the purpose of establishing an adaptive management framework, hard lines have been drawn between the technical elements of the AMA. However, in practice, the rapid and seamless interaction between the elements can blur these lines. For example, to aid in the analysis and interpretation of site characterization data, a static geocellular model is often required. While this model development is part of the technical element, modeling and simulation, it is an integral part of the site characterization effort. Likewise, much of the monitoring data collected as part of the MVA technical element can be used to inform site characterization. This back-and-forth flow of data and use of models between the technical elements continues throughout the project.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗

DOE OSTI2025

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY↗

NASA NTRS1983

Integrating computer programs for engineering analysis and design

The design of a third-generation system for integrating computer programs for engineering and design has been developed for the Aerospace Vehicle Interactive Design (AVID) system. This system consists of an engineering data management system, program interface software, a user interface, and a geometry system. A relational information system (ARIS) was developed specifically for the computer-aided engineering system. It is used for a repository of design data that are communicated between analysis programs, for a dictionary that describes these design data, for a directory that describes the analysis programs, and for other system functions. A method is described for interfacing independent analysis programs into a loosely-coupled design system. This method emphasizes an interactive extension of analysis techniques and manipulation of design data. Also, integrity mechanisms exist to maintain database correctness for multidisciplinary design tasks by an individual or a team of specialists. Finally, a prototype user interface program has been developed to aid in system utilization.

Wilhite, A. W.↗

NASA NTRS2011

Land and Atmosphere Near-Real-Time Capability for Earth Observing System

The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.

Murphy, Kevin J.↗

DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗