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

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems The National Academies have prioritized the pursuit of new scientific discoveries using diverse and temporally coordinated measurements from multiple ground and space-based observatories. Networked communications can enable such measurements by connecting individual observatories and allowing them to operate as a cohesive and purposefully designed system. Timely data flows across terrestrial and space communications networks are required to observe transient scientific events and processes. Currently, communications to space-based observatories experience large latencies due to manual service reservation and scheduling procedures, intermittent signal coverage, and network capacity constraints. If space communications network latencies could be reduced, new discoveries about dynamic scientific processes could be realized. However, science mission and network planners lack a systematic framework for defining, quantifying and evaluating timely space data flow implementation options for transient scientific observation scenarios involving multiple ground and space-based observatories. This dissertation presents a model-based systems engineering approach to investigate and develop network solutions to meet the needs of transient science space systems. First, a systematic investigation of the current transient science operations of the National Aeronautics and Space Administration’s (NASA) Tracking and Data Relay Satellite (TDRS) space data network and the Neil Gehrels Swift Observatory resulted in a formal architectural model for transient science space systems. Two methods individual missions may use to achieve timely network services were defined, quantitatively modeled, and experimentally compared. Next, the architectural model was extended to describe two alternative ways to achieve timely and autonomous space data flows to multiple space-based observatories within the context of a purposefully designed transient science observation scenario. A quantitative multipoint space data flow modeling method based in queueing theory was defined. General system suitability metrics for timeliness, throughput, and capacity were specified to support the evaluation of alternative network data flow implementations. A hypothetical design study was performed to demonstrate the multipoint data flow modeling method and to evaluate alternative data flow implementations using TDRS. The merits of a proposed future TDRS broadcast service to implement multipoint data flows were quantified and compared to expected outcomes using the as-built TDRS network. Then, the architectural model was extended to incorporate commercial network service providers. Quantitative models for Globalstar and Iridium short messaging data services were developed based on publicly available sources. Financial cost was added to the set of system suitability metrics. The hypothetical design study was extended to compare the relative suitability of the as-built TDRS network with the commercial Globalstar and Iridium networks. Finally, results from this research are being applied by NASA missions and network planners. In 2020, Swift implemented the first automated command pipeline, increasing its expected gravitational wave follow-up detection rate by greater than 400%. Current NASA technology initiatives informed by this research will enable future space-based observatories to become interoperable sensing devices connected by a diverse ecosystem of network service providers.

Christopher J. Roberts↗
NASA NTRSDate not supplied

Lean Model-Based Systems Engineering on the NASA High-Density Vertiplex Subproject

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE)in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers(SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This paper will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V)tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This paper details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.

Demetrios Katsaduros↗
NASA NTRS2022

A Systems Model for System-Wide Safety Safety Demonstrator (SD-1): Wildfire Response Operations

The aim of this internship-based project was to contribute to the ongoing development of a systems model for System-Wide Safety’s first Technical Challenge 5 (TC5) series Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System (IASMS) in emerging wildfire response operations. Using Models-Based Systems Engineering (MBSE) principles to develop the model, I organized and traced previously collected stakeholder needs from the Spring 2022 NASA System-Wide Safety Wildland Firefighting Operations Virtual Workshop (https://nari.arc.nasa.gov/sws-wildfire) to system elements, creating connections which can be used in the future by the project engineers to identify and address requirements gaps throughout the system design process. I also identified and modeled preliminary use case scenarios for aerial assets in the demonstrator and, building on previously produced preliminary high-level models of the 8 SD-1 Services, Functions, and Capabilities (SFCs) and their IASMS data flows, worked to model the Real-Time Risk Assessment (RTRA) tool as an implementation of Risk Assessment and Management that can take in multiple sets of data monitored by SFCs. Project deliverables include stakeholder requirements tables and matrices and systems model diagrams produced with MagicDraw software in the SysML Systems Modeling Language, with eventual plans to connect model diagrams to a Department of Defense Operational Viewpoint (OV-1) graphic, a high-level operational concept graphic that will be used to visualize the SD-1 scenarioin a future phase. The system model serves to provide a common understanding of the scope of and activities necessary for the completion of SD-1,and traces how stakeholder needs are to be addressed.

model-based systems engineering↗
NASA NTRSDate not supplied

Developing and Testing a Common Space Systems Ontology using the Ontological Modeling Language

This paper describes the development and testing of the initial version of a common space systems ontology (CoSSO) for use by the Advanced Concepts Office (ACO) at NASA's Marshall Space Flight Center. The ontology provides a shared conceptualization of concepts of interest to the ACO for modeling aerospace systems concepts in a pre-phase A context to aid with the transition to a more model-based paradigm. The ontological concepts and relations, as well as the anticipated use cases, were developed through interactions with the relevant subject matter experts at the ACO and implemented in the Ontological Modeling Language (OML). The ontology builds on the Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). While most of the ontology is still in the initial stages, an Environmental Control and Life Support System (ECLSS) ontology is being built on top of the main CoSSO and heavily developed as a proof of concept. The ECLSS ontology is designed with different use cases in mind, namely predicting and diagnosing errors in ECLS systems on long-duration missions, with a focus on the Four-Bed CO$_2$ carbon dioxide scrubber currently on board the ISS. The ECLSS ontology is being developed in a similar manner to the CoSSO, and designed to be compatible with it. The current state of both ontologies is presented and discussed, along with plans for future development and testing.

Conceptual Design↗
NASA NTRSDate not supplied

Lean Model-Based Systems Engineering on the NASA High-Density Vertiplex Subproject

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers (SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This presentation will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V) tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This presentation details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.

systems engineering↗
NASA NTRS2021

IceNode: A Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements Under Ice Shelves

Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean and significantly raising global sea level. However, the accelerating dynamics of ice shelf melt in a warming environment are poorly understood, and the collapse of Antarctic ice shelves remains one of the largest sources of uncertainty in global sea level rise projections. The cavities below Antarctic ice shelves are notoriously difficult to access, making model-based hypotheses about the relationship between ocean warming and greater ice shelf melting difficult to verify because of a lack of in-situ data to constrain model parameters and examine key assumptions. We present early progress on IceNode, a novel vehicle under development at the NASA Jet Propulsion Laboratory designed to acquire well-distributed, concurrent, long-duration melt rate measurements under ice shelves. IceNodes are deployed as an array from a ship at the shelf edge, and use variable buoyancy to ride melt-driven exchange currents far into the cavity. Once underneath their target, they release a ballast weight to become highly positively buoyant and attach to the underside of the ice shelf, where they acquire in-situ measurements of basal melt rate directly at the ice-ocean interface for a year or more. Finally, IceNodes detach from their landing structure and use variable buoyancy to ride melt-driven exchange currents back to open water, where they surface and transmit their mission data home. IceNodes are designed to be relatively low-cost, expendable, and have simple logistics, enabling scientists to deploy scalable arrays that simultaneously measure co-varying ice shelf melt and ocean conditions over large spatial areas, thereby providing an unprecedented view of ice shelf melt rate variability and its drivers.

Zapien, Xavier↗
NASA NTRSDate not supplied

Initial Development of A Digital Twin Model for an Electrified Aircraft Propulsion Emulation Rig

In support of aviation fuel burn and emission reduction goals, NASA is pursing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP), which relies on the generation, storage, transmission, and use of electrical power for producing thrust and optimizing propulsion system efficiency. Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes advances in propulsion controls, which will be vital for ensuring coordinated efficient operation of the complex integrated subsystems that comprise EAP architectures. To support EAP controls research, the NASA Glenn Research Center has developed the Hybrid Propulsion Emulation Rig (HyPER). The HyPER laboratory hardware includes shaft-mounted electric machines, power converters, power supplies, power distribution cables, and an energy storage device that can be reconfigured to represent a variety of EAP architectures. It also includes an integrated real-time computer system that hosts developed EAP control software and turbomachinery simulations. This enables the electrical system and rotating shafts of EAP designs to be implemented in actual hardware and integrated with turbomachinery simulations and system-level EAP control logic implemented in software. In this form, the HyPER laboratory provides a partially simulated, partially hardware-in-the-loop test environment enabling the initial development and evaluation of EAP control technology. A prerequisite for the development of EAP control designs is the availability of a system model that accurately reflects the operation of the electrical system hardware. To support this need, a digital twin model of the HyPER electrical system hardware is under development. This model is being coded in the MATLAB Simulink environment and uses the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT) to construct a digital twin framework. EMTAT contains generic electrical component building blocks that are simulated at turbomachinery timescales. Associated inputs and outputs allow the blocks to be combined to model complete electrical systems. The EMTAT blocks also contain adjustable internal maps and parameters that can be set to reflect the operation of a specific electrical component. For the HyPER digital twin, the settings of these EMTAT block internal maps and parameters is determined through machine learning approaches applied to characterization run data collected from the laboratory. During characterization runs the laboratory electrical system hardware is subjected to a full range of torque, speed, and power settings. Acquired data is then used to estimate EMTAT block parameters using a variety of machine learning techniques. The resulting digital twin model is found to match the operation of actual HyPER hardware with an accuracy suitable for control development purposes. It also holds promise for other applications including modeling the performance of HyPER laboratory reconfigurations and model-based anomaly detection. Planned follow-on work to automate post-processing of acquired laboratory data to update the HyPER digital twin model will also be presented and discussed.

Electrified Aircraft Propulsion↗
NASA NTRS2024

An Approach to Evaluating the Impact of Small-Core Turbofan Technologies on Engine and Aircraft Performance

NASA’s Hybrid Thermally Efficient Core (HyTEC) project aims to accelerate the development of small-core turbofan engine technologies to enable a fuel burn reduction of 5 to 10 percent for next-generation aircraft, compared to 2020s best-in-class technology. This paper presents a demonstration of methods for evaluating the potential performance impact of small-core engine technologies developed under Phase 1 of the HyTEC project. The approach involves model-based systems analysis, where small-core innovations are integrated into baseline turbofan and aircraft systems models, creating a notional vision system. Performance of the vision system is examined at both the engine and vehicle level. The examined performance metrics include: engine bypass ratio, overall pressure ratio, high pressure compressor exit corrected mass flow, and aircraft fuel burn. The CFM LEAP-1B28 and Boeing 737 MAX 8 are chosen as the baseline state-of-the-art systems. Preliminary results map a design space for the small-core vision system and quantify the distinct effects of technologies on the key metrics.

Michael A Bennett↗
NASA NTRSDate not supplied

Design, Formalization, and Verification of Decision Making for Intelligent Systems

The development of autonomous systems requires a rigorous process that can guarantee a system’s reliability in critical applications. At its core, an autonomous system bases its behavior on a well-defined decision making system. In this paper, we present a methodological basis for the design, formalization and formal verification of Decision Making systems for autonomous agents. The approach is generally applicable to operational objectives that can be functionally decomposed and subsequently represented as Hierarchical Finite State Machines. As a case study, we present the application of this method to implement a Decision Making model in Simulink. Furthermore, we present how we use NASA’s FRET tool to write requirements in structured natural language and generate formal specifications that can be automatically digested by NASA’s CoCoSim tool. Finally, we present how, by leveraging CoCoSim, we perform formal verification against the Simulink model and present analysis results.

Model-based development↗
NASA NTRS2024

Summary of Model-based Attitude Control of LUVOIR in Modular Dynamic Analysis (MDA) Simulink Environment

This document overviews the work I completed in the second half of my summer 2018 internship experience in Code 591 at NASA Goddard Space Flight Center. Please see the former memorandum for additional context on the project. The take away point is that LUVOIR A has been modeled as three rigid bodies linked by 1-DOF rotary joints. An LQR attitude controller was designed for precision pointing of the spacecraft with the design requirement that steady state oscillations have amplitude less than a miliarcsecond. The performance of the algorithm was tested in the Modular Dynamic Analysis Simulink library developed by J. Roger Chen at NASA Goddard. While the initial simulations were of rigid bodies, subsequent simulations included flexible body modes on all three bodies of the model. The simulated structural deformations initially destabilized the LQR controller thus requiring a redesign of the K gain matrix. The final simulation demonstrated a stabilizing feedback gain with miliarcsecond precision within 400 minutes. This run used 20 modes on the spacecraft, 9 on the bus, and 100 on the payload. Future recommended work includes the development of a Vibration Isolation and Precision Pointing System (VIPPS) Simulink model. Armed with this model, the system should be modeled as four bodies whose associated flexible files must be generated from the spacecraft through Gimbal 1, Gimbal 1 through Gimbal 2, Gimbal 2 through the VIPPS interface, and the VIPPS interface through the payload.

William Bentz↗
NASA NTRS2024

Teachers’ Use and Adaptation of A Model-Based Climate Curriculum: A Three-Year Longitudinal Study

Foregrounding climate education in formal science learning environments provides students with opportunities to develop critical climate-related knowledge and skills. However, research has shown many challenges to teaching and learning about Earth’s climate and global climate change (GCC). This longitudinal study aims to establish how secondary science teachers, over time, implement model-based climate curricula in support of students’ climate and GCC education by utilizing EzGCM. The model (EzGCM) is a data-driven, computer-based climate modeling tool use to explore global climate data. Multiple sources of data collection, including teacher interviews, classroom observations, and daily reflections, were employed to address the research question: “How did two teachers’ implementation strategies evolve over the three-year study while utilizing a model-based, climate-focused curriculum?” This study provides insight into how and why these resources [model-based climate education curricula] are utilized in science learning environments, thereby informing ongoing efforts to enhance climate education and, in doing so, preparing the next generation of climate-literate adults prepared to confront this most critical global challenge of our age. The findings showed while both teachers engaged in increasingly model-centric instructional practices, these changes were modest. Furthermore, both teacher’s observed classroom practices were less model-centric than the designed curriculum. Ultimately emphasizing the transition from existing practices to improved ones, rather than seeking the perfect approach, the study offers practical insights that can honestly assist secondary educators in real-world settings by highlighting state of climate education in secondary science classrooms.

Secondary science teaching↗
NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modeling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modeling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modeling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modeling languages

Hazard Assessment↗
NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide little recommendation on how to represent the function of the system to perform FHA, or rely on readily-available models with little justification in design theory. This paper presents some of the problems with current modelling languages used for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis, as well as desirable principles an FHA-supporting analysis language should embody. It further introduces the Functional Reasoning Design Language (FRDL), a formal modelling language for describing the functional behaviors of a system and their interactions which satisfies these principles. To demonstrate the use of this language, the modelling and hazard analysis of a disaster response drone is presented.

safety analysis↗
NASA NTRSDate not supplied

A Novel Additive Manufacturing Process Metric for Predicting Spatter-related Porosity in Laser Powder Bed Fusion

Components fabricated using the powder bed fusion laser beam metallic (PBF) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Porosity defects resulting from weld pass inconsistencies are a concern for PBF materials and have a strong adverse effect on the mechanical properties. In an effort to understand and avoid spatter induced porosity, this work aims to provide a novel computational technique that can be used to quantify and predict the sequence-sensitive impact of spatter upon the PBF process stability and consequential porosity in as-printed material. A novel spatter impact AM model-based process metric (AM-PM) is introduced to model and quantify the impact that spatter has on the PBF process. The occurrence of spatter induced porosity is studied using thermal rise, lack of fusion, and spatter impact AM-PMs synchronized with porosity measured by high-resolution X-ray computed tomography. Six cylindrical specimens were designed to compare the AM-PM correlations with porosity across two different laser powers and three hatch scanning strategies. A point field based computational approach is shown to be effective for testing the influence of the different AM-PMs and interrogating the physical process conditions underlying the porosity formation. Further, characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated with the spatter impact AM-PM, which can be exploited to predict and control spatter related porosity by the hatch strategy. Efforts to predict porosity composition within PBF material should incorporate the PBF process disturbances that can result from spatter. The spatter impact AM-PM can be readily incorporated into the development of defect prediction models at the part scale.

Additive Manufacturing↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗