Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “domain expertise”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

SIRE: A Simple Interactive Rule Editor for NICBES

To support evolution of domain expertise, and its representation in an expert system knowledge base, a user-friendly rule base editor is mandatory. The Nickel Cadmium Battery Expert System (NICBES), a prototype of an expert system for the Hubble Space Telescope power storage management system, does not provide such an editor. In the following, a description of a Simple Interactive Rule Base Editor (SIRE) for NICBES is described. The SIRE provides a consistent internal representation of the NICBES knowledge base. It supports knowledge presentation and provides a user-friendly and code language independent medium for rule addition and modification. The SIRE is integrated with NICBES via an interface module. This module provides translation of the internal representation to Prolog-type rules (Horn clauses), latter rule assertion, and a simple mechanism for rule selection for its Prolog inference engine.

Bykat, Alex↗
NASA NTRS1991

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed.

Loftin, R. Bowen↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
NASA NTRS2016

The Trick Simulation Toolkit: A NASA/Opensource Framework for Running Time Based Physics Models

The Trick Simulation Toolkit is a simulation development environment used to create high fidelity training and engineering simulations at the NASA Johnson Space Center and many other NASA facilities. Its purpose is to generate a simulation executable from a collection of user-supplied models and a simulation definition file. For each Trick-based simulation, Trick automatically provides job scheduling, numerical integration, the ability to write and restore human readable checkpoints, data recording, interactive variable manipulation, a run-time interpreter, and many other commonly needed capabilities. This allows simulation developers to concentrate on their domain expertise and the algorithms and equations of their models. Also included in Trick are tools for plotting recorded data and various other supporting utilities and libraries. Trick is written in C/C++ and Java and supports both Linux and MacOSX computer operating systems. This paper describes Trick's design and use at NASA Johnson Space Center.

Penn, John M.↗
NASA NTRS2016

Marshall Space Flight Center Ground Systems Development and Integration

Ground Systems Development and Integration performs a variety of tasks in support of the Mission Operations Laboratory (MOL) and other Center and Agency projects. These tasks include various systems engineering processes such as performing system requirements development, system architecture design, integration, verification and validation, software development, and sustaining engineering of mission operations systems that has evolved the Huntsville Operations Support Center (HOSC) into a leader in remote operations for current and future NASA space projects. The group is also responsible for developing and managing telemetry and command configuration and calibration databases. Personnel are responsible for maintaining and enhancing their disciplinary skills in the areas of project management, software engineering, software development, software process improvement, telecommunications, networking, and systems management. Domain expertise in the ground systems area is also maintained and includes detailed proficiency in the areas of real-time telemetry systems, command systems, voice, video, data networks, and mission planning systems.

Wade, Gina↗
NASA NTRS2017

Virtualized Multi-Mission Operations Center (vMMOC) and its Cloud Services

His presentation will cover, the current and future, technical and organizational opportunities and challenges with virtualizing a multi-mission operations center. The full deployment of Goddard Space Flight Centers (GSFC) Virtualized Multi-Mission Operations Center (vMMOC) is nearly complete. The Space Science Mission Operations (SSMO) organizations spacecraft ACE, Fermi, LRO, MMS(4), OSIRIS-REx, SDO, SOHO, Swift, and Wind are in the process of being fully migrated to the vMMOC. The benefits of the vMMOC will be the normalization and the standardization of IT services, mission operations, maintenance, and development as well as ancillary services and policies such as collaboration tools, change management systems, and IT Security. The vMMOC will also provide operational efficiencies regarding hardware, IT domain expertise, training, maintenance and support.The presentation will also cover SSMO's secure Situational Awareness Dashboard in an integrated, fleet centric, cloud based web services fashion. Additionally the SSMO Telemetry as a Service (TaaS) will be covered, which allows authorized users and processes to access telemetry for the entire SSMO fleet, and for the entirety of each spacecrafts history. Both services leverage cloud services in a secure FISMA High and FedRamp environment, and also leverage distributed object stores in order to house and provide the telemetry. The services are also in the process of leveraging the cloud computing services elasticity and horizontal scalability. In the design phase is the Navigation as a Service (NaaS) which will provide a standardized, efficient, and normalized service for the fleet's space flight dynamics operations. Additional future services that may be considered are Ground Segment as a Service (GSaaS), Telemetry and Command as a Service (TCaaS), Flight Software Simulation as a Service, etc.

Ido, Haisam Kassim↗
NASA NTRS2017

Risk-Significant Adverse Condition Awareness Strengthens Assurance of Fault Management Systems

As spaceflight systems increase in complexity, Fault Management (FM) systems are ranked high in risk-based assessment of software criticality, emphasizing the importance of establishing highly competent domain expertise to provide assurance. Adverse conditions (ACs) and specific vulnerabilities encountered by safety- and mission-critical software systems have been identified through efforts to reduce the risk posture of software-intensive NASA missions. Acknowledgement of potential off-nominal conditions and analysis to determine software system resiliency are important aspects of hazard analysis and FM. A key component of assuring FM is an assessment of how well software addresses susceptibility to failure through consideration of ACs. Focus on significant risk predicted through experienced analysis conducted at the NASA Independent Verification & Validation (IV&V) Program enables the scoping of effective assurance strategies with regard to overall asset protection of complex spaceflight as well as ground systems. Research efforts sponsored by NASAs Office of Safety and Mission Assurance (OSMA) defined terminology, categorized data fields, and designed a baseline repository that centralizes and compiles a comprehensive listing of ACs and correlated data relevant across many NASA missions. This prototype tool helps projects improve analysis by tracking ACs and allowing queries based on project, mission type, domain/component, causal fault, and other key characteristics. Vulnerability in off-nominal situations, architectural design weaknesses, and unexpected or undesirable system behaviors in reaction to faults are curtailed with the awareness of ACs and risk-significant scenarios modeled for analysts through this database. Integration within the Enterprise Architecture at NASA IV&V enables interfacing with other tools and datasets, technical support, and accessibility across the Agency. This paper discusses the development of an improved workflow process utilizing this database for adaptive, risk-informed FM assurance that critical software systems will safely and securely protect against faults and respond to ACs in order to achieve successful missions.

IV&V↗
NASA NTRS2017

Risk-Significant Adverse Condition Awareness Strengthens Assurance of Fault Management Systems

As spaceflight systems increase in complexity, Fault Management (FM) systems are ranked high in risk-based assessment of software criticality, emphasizing the importance of establishing highly competent domain expertise to provide assurance. Adverse conditions (ACs) and specific vulnerabilities encountered by safety- and mission-critical software systems have been identified through efforts to reduce the risk posture of software-intensive NASA missions. Acknowledgement of potential off-nominal conditions and analysis to determine software system resiliency are important aspects of hazard analysis and FM. A key component of assuring FM is an assessment of how well software addresses susceptibility to failure through consideration of ACs. Focus on significant risk predicted through experienced analysis conducted at the NASA Independent Verification Validation (IVV) Program enables the scoping of effective assurance strategies with regard to overall asset protection of complex spaceflight as well as ground systems. Research efforts sponsored by NASA's Office of Safety and Mission Assurance defined terminology, categorized data fields, and designed a baseline repository that centralizes and compiles a comprehensive listing of ACs and correlated data relevant across many NASA missions. This prototype tool helps projects improve analysis by tracking ACs and allowing queries based on project, mission type, domaincomponent, causal fault, and other key characteristics. Vulnerability in off-nominal situations, architectural design weaknesses, and unexpected or undesirable system behaviors in reaction to faults are curtailed with the awareness of ACs and risk-significant scenarios modeled for analysts through this database. Integration within the Enterprise Architecture at NASA IVV enables interfacing with other tools and datasets, technical support, and accessibility across the Agency. This paper discusses the development of an improved workflow process utilizing this database for adaptive, risk-informed FM assurance that critical software systems will safely and securely protect against faults and respond to ACs in order to achieve successful missions.

Fault management↗
NASA NTRS2015

A Principled Approach to the Specification of System Architectures for Space Missions

Modern space systems are increasing in complexity and scale at an unprecedented pace. Consequently, innovative methods, processes, and tools are needed to cope with the increasing complexity of architecting these systems. A key systems challenge in practice is the ability to scale processes, methods, and tools used to architect complex space systems. Traditionally, the process for specifying space system architectures has largely relied on capturing the system architecture in informal descriptions that are often embedded within loosely coupled design documents and domain expertise. Such informal descriptions often lead to misunderstandings between design teams, ambiguous specifications, difficulty in maintaining consistency as the architecture evolves throughout the system development life cycle, and costly design iterations. Therefore, traditional methods are becoming increasingly inefficient to cope with ever-increasing system complexity. We apply the principles of component-based design and platform-based design to the development of the system architecture for a practical space system to demonstrate feasibility of our approach using SysML. Our results show that we are able to apply a systematic design method to manage system complexity, thus enabling effective data management, semantic coherence and traceability across different levels of abstraction in the design chain. Just as important, our approach enables interoperability among heterogeneous tools in a concurrent engineering model based design environment.

platform-based design↗
NASA NTRS2016

Topic Modeling of NASA Space System Problem Reports: Research in Practice

Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires nontrivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless,topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.

Data Mining↗
NASA NTRS2020

Stereo Combining NASA Technologies and Partnerships to Transform Current-Day Emergency Response Operations

STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an overview of the STEReO project and introduces a stakeholder workshop as a three-day activity to solicit input from the community of emergency response operators and related industry representatives.

Mercer, Joey S.↗
NASA NTRS2020

STEReO: Combining NASA Technologies and Partnerships to Transform Current-Day Emergency Response Operations

STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA?s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the STEReO project to attendees of the annual North American Aerial Fire Fighting conference (AFFNA 2020).

Mercer, Joey S.↗
NASA NTRS2020

Stereo: Combining NASA Technologies and Partnerships to Transform Current-Day Emergency Response Operations

STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA's UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the STEReO project to the members of the US Forest Service (USFS) region 5 aerial supervisor steering committee (ASSC).

Mercer, Joey↗
NASA NTRS2020

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Memarzadeh, Milad↗
NASA NTRS2021

Topic Modeling Tool for PeTaL (Periodic Table of Life)

A topic modeling tool is constructed for the purpose of providing insights from biology to the engineer within the framework of PeTaL (Periodic Table of Life). The machine learning text mining tools–latent Dirichlet allocation (LDA) and nonnegative matrix factorization (NMF) with Kullback-Leibler (KL) divergence—are used to provide topic clusters to the user. Topic clusters are the underlying themes of a paper. For the text modeling problem, NMF-KL is the equivalent of probabilistic latent semantic analysis. Both LDA and NMF-KL are top-performing modeling tools. These tools are used to identify biological specimens relevant to the user. Various organisms solve a particular survival problem in nature differently. The topic clusters allow people without domain expertise to find these cross-topic themes in the body of documents and then branch out and examine papers whose target organisms solve the engineer’s problem. Abstracts from the Journal of Experimental Biology were used as input for the clustering tool in addition to a curated set of articles for validation. The tool is able to accept alternate input sources.

Machine learning↗
NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Scalable Traffic Management for Emergency Response Operations (STEReO)

STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the STEReO project to attendees of the AIAA Aviation conference.

emergency response operations↗
NASA NTRSDate not supplied

Exploring data-driven modeling of boundary layer transition

Prediction of laminar-turbulent transition in boundary layer flows is an important component of predicting the aerodynamic performance of a number of aerospace configurations. According to the CFD Vision 2030 [1], transition modeling represents acriticalarea in CFD simulation capability that will remain a pacing item for the foreseeable future. The fact thattransition can take placevia either one of a myriad possible paths adds to the challenges inreliable transition predictions, despite a limited knowledge of the relevant input parameters. In the low disturbance environments typical of flight applications, transition is often initiated by small amplitude disturbances in the form of linear instability waves of the laminar boundary layer. These disturbances amplify linearly at first and eventually undergo a sequence of nonlinear interactions that result in transition to turbulence. Because the nonlinear phase is rather rapid, the amplification of boundary layer instabilities is governed by the linearstability theory over a majority of the distance leading up to the onset of transition. Semi-empirical transition correlations based on the linear stability theory have been successful in explaining the observed trends in transition location within a broad class of flows. However, the application of stability theory is highly non-robust and often requires a significant domain expertise. Recent work at the NASA Langley Research Center has beenaimed at bridging the gap between physics based transition analyses such as those based on linear stability theory and practical applications that require transition prediction by users that may not be well versed in transition physics. The applications of deep learning have been at the center of these efforts. This presentation will focus on the progress achieved thus far, highlighting the applications of neural networks to selectedtransition scenarios across a range of Mach numbers and flow configuration, as well as the lessons learnedand remaining challengeswithrespect to the selection of training data and neural networks architectures, hyperparameter tuning, and the physical insights distilled from the otherwise black-box models.

M. R. Malik↗