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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Streamlining Payload Integration

Payload integration onto space transport vehicles and the International Space Station (ISS) is a complex process. Yet, cargo transport is the sole reason for any space mission, be it for ferrying humans, science, or hardware. As the largest such effort in history, the ISS offers a wide variety of payload experience. However, for any payload to reach the Space Station under the current process, Payload Developers face a list of daunting tasks that go well beyond just designing the payload to the constraints of the transport vehicle and its stowage topology. Payload customers are required to prove their payload s functionality, structural integrity, and safe integration - including under less than nominal situations. They must also plan for or provide training, procedures, hardware labeling, ground support, and communications. In addition, they must deal with negotiating shared consumables, integrating software, obtaining video, and coordinating the return of data and hardware. All the while, they must meet export laws, launch schedules, budget limits, and the consensus of more than 12 panel and board reviews. Despite the cost and infrastructure overhead, payload proposals have increased. Just in the span from FY08 to FY09, the NASA Payload Space Station Support Office budget rose from $78M to $96M in attempt to manage the growing manifest, but the potential number of payloads still exceeds available Payload Integration Management manpower. The growth has also increased management difficulties due to the fact that payloads are more frequently added to a flight schedule late in the flow. The current standard ISS template for payload integration from concept to payload turn-over is 36 months, or 18 months if the payload already has a preliminary design. Customers are increasingly requiring a turn-around of 3 to 6-months to meet market needs. The following paper suggests options for streamlining the current payload integration process in order to meet customer schedule needs and reduce costs for both the integration support teams and the developers, without reducing quality or compromising safety. Issues for the key integration areas of planning, training, verification, and safety are presented in a Root-Cause Analysis study, with plausible solutions provided that involve technology and tools already available to the ISS community. Although based upon the ISS process, the payload integration techniques outlined herein also offer an integration template for any space transport endeavor.

Lufkin, Susan N.↗
NASA NTRS2010

Smart Sensor Demonstration Payload

Sensors are a critical element to any monitoring, control, and evaluation processes such as those needed to support ground based testing for rocket engine test. Sensor applications involve tens to thousands of sensors; their reliable performance is critical to achieving overall system goals. Many figures of merit are used to describe and evaluate sensor characteristics; for example, sensitivity and linearity. In addition, sensor selection must satisfy many trade-offs among system engineering (SE) requirements to best integrate sensors into complex systems [1]. These SE trades include the familiar constraints of power, signal conditioning, cabling, reliability, and mass, and now include considerations such as spectrum allocation and interference for wireless sensors. Our group at NASA s John C. Stennis Space Center (SSC) works in the broad area of integrated systems health management (ISHM). Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces to operators and other system elements [2]. Sensor technologies are the base fabric that feed data and health information to higher layers. Cost-effective operation of the complement of test stands benefits from technologies and methodologies that contribute to reductions in labor costs, improvements in efficiency, reductions in turn-around times, improved reliability, and other measures. ISHM is an active area of development at SSC because it offers the potential to achieve many of those operational goals [3-5].

Schmalzel, John↗
NASA NTRS2012

Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

Figueroa, Jorge F.↗
NASA NTRS2012

Photogrammetry Measurements During a Tanking Test on the Space Shuttle External Tank, ET-137

On November 5, 2010, a significant foam liberation threat was observed as the Space Shuttle STS-133 launch effort was scrubbed because of a hydrogen leak at the ground umbilical carrier plate. Further investigation revealed the presence of multiple cracks at the tops of stringers in the intertank region of the Space Shuttle External Tank. As part of an instrumented tanking test conducted on December 17, 2010, a three dimensional digital image correlation photogrammetry system was used to measure radial deflections and overall deformations of a section of the intertank region. This paper will describe the experimental challenges that were overcome in order to implement the photogrammetry measurements for the tanking test in support of STS-133. The technique consisted of configuring and installing two pairs of custom stereo camera bars containing calibrated cameras on the 215-ft level of the fixed service structure of Launch Pad 39-A. The cameras were remotely operated from the Launch Control Center 3.5 miles away during the 8 hour duration test, which began before sunrise and lasted through sunset. The complete deformation time history was successfully computed from the acquired images and would prove to play a crucial role in the computer modeling validation efforts supporting the successful completion of the root cause analysis of the cracked stringer problem by the Space Shuttle Program. The resulting data generated included full field fringe plots, data extraction time history analysis, section line spatial analyses and differential stringer peak ]valley motion. Some of the sample results are included with discussion. The resulting data showed that new stringer crack formation did not occur for the panel examined, and that large amounts of displacement in the external tank occurred because of the loads derived from its filling. The measurements acquired were also used to validate computer modeling efforts completed by NASA Marshall Space Flight Center (MSFC).

Littell, Justin D.↗
NASA NTRS2021

Arecibo Observatory Auxiliary M4N Socket Termination Failure Investigation

The NASA Engineering and Safety Center (NESC) was requested to support the Arecibo Observatory failure investigation in determining the root cause of the Auxiliary M4N cable failure. The NESC and Kennedy Space Center led an integrated NASA investigation with support from Marshall Space Flight Center and The Aerospace Corporation that included forensic investigation of failed hardware, finite element modeling, materials characterization, and root cause analysis. This document contains the outcome of the NESC Assessment.

Zinc Spelter Sockets↗
NASA NTRS2020

A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot’s abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi- robot system autonomously schedules which robots should perform a given task and when, using its worldview–the robot’s internal representation of its belief about the environment and other robots’ states. A key problem for operators is that robots’ worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots’ scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots’ scheduling decisions, detecting de-synchronizations, and pinpointing their cause by comparing every robot’s worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots’ schedules and (ii) detect and conduct a root cause analysis of the robots’ desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, a formative study was performed to identify the necessary system design requirements, which supports the design of the system. A qualitative study with 12 roboticists reveals that MOSAIC Viewer is faster- and easier-to-use than the users’ current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots’ schedules and detect and pin-point the cause of desynchronized worldviews.

Ma, Kwan-Liu↗
NASA NTRS2023

BERT-Based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

Hazard analysis↗
NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗
NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗
NASA NTRS2005

Space Shuttle Operations and Infrastructure: A Systems Analysis of Design Root Causes and Effects

This NASA Technical Publication explores and documents the nature of Space Shuttle operations and its supporting infrastructure and addresses fundamental questions often asked of the Space Shuttle program why does it take so long to turnaround the Space Shuttle for flight and why does it cost so much? Further, the report provides an overview of the cause-and effect relationships between generic flight and ground system design characteristics and resulting operations by using actual cumulative maintenance task times as a relative measure of direct work content. In addition, this NASA TP provides an overview of how the Space Shuttle program's operational infrastructure extends and accumulates from these design characteristics. Finally, and most important, the report derives a set of generic needs from which designers can revolutionize space travel from the inside out by developing and maturing more operable and supportable systems.

McCleskey, Carey M.↗
NASA NTRS2019

Achieving Improved Reliability with Failure Analysis

Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC (Professional Development Course) starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs (Printed Circuit Boards), PCBAs (Printed Circuit Board Assembly) and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA (Failure Modes and Effects Analysis), fishbone (Cause-And-Effect Diagram), FTA (Fault Tree Analysis)), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Attendees will Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM (Confocal Scanning Electron Microscopy) etc.) -Destructive analysis (DPA (Destructive Physical Analysis), Decap (Decapsulation), FIB (Focused Ion Beam) etc.) -Materials characterization (XRF (X-Ray Fluorescence) , EDS (Error Detection Sequential), TMA/DSC (Thermal Mechanical Analysis/Differential Scanning Calorimetry) etc.)

PCB quality↗
NASA NTRS2018

Achieving Improved Reliability with Failure Analysis

Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs, PCBAs and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA, fishbone, FTA), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Will You Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM etc.) -Destructive analysis (DPA, Decap, FIB etc.) -Materials characterization (XRF, EDS, TMA/DSC etc.) Who Will Benefit: Reliability engineers, failure analysis engineers, engineering managers, design engineers, component engineers, quality assurance functions and, personnel involved with reliability activities within their company.

non-destructive techniques↗
NASA NTRS2013

Memory Circuit Fault Simulator

Spacecraft are known to experience significant memory part-related failures and problems, both pre- and postlaunch. These memory parts include both static and dynamic memories (SRAM and DRAM). These failures manifest themselves in a variety of ways, such as pattern-sensitive failures, timingsensitive failures, etc. Because of the mission critical nature memory devices play in spacecraft architecture and operation, understanding their failure modes is vital to successful mission operation. To support this need, a generic simulation tool that can model different data patterns in conjunction with variable write and read conditions was developed. This tool is a mathematical and graphical way to embed pattern, electrical, and physical information to perform what-if analysis as part of a root cause failure analysis effort.

Sheldon, Douglas J.↗
NASA NTRS2013

A System for Fault Management for NASA's Deep Space Habitat

NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations. One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy.

Fault management↗
NASA NTRS2013

A System for Fault Management and Fault Consequences Analysis for NASA's Deep Space Habitat

NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations. One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy

System Effects Analysis↗
NASA NTRS2018

Building Reliable Printed Circuit Boards - the Lessons Learned

"Printed circuit boards (PCBs) are the baseline for electronics manufacturing upon which electronic components are mounted and formed into electronic systems. PCBs are used in a variety of electronic circuits from simple one-transistor amplifiers to large super computers. A PCB serves three main functions: 1) it provides the necessary mechanical support for the components in the circuit 2) it provides the necessary electrical interconnections, and 3) it bears some form of legend which identifies the components it carries. The failure modes on the PCBs can be categorized in a hierarchical structure, in which the mechanisms and causes are site or location dependant. This one day workshop will discuss a variety of failure mechanisms that effect the functionality of PCBs. These mechanisms can be related to how PCB materials are selected, PCBs are designed, manufactured, tested and used in the field conditions. The workshop will begin with an overview of PCB manufacturing, materials and processes. With the help of examples and case studies, a wide range of failure mechanisms will be discussed, case studies are focused on digital circuits, however some failures in analog, double sided boards are also presented. The workshop then provides the guideline for selection of methodologies for identifying potential failure mechanisms based on the failure history and how a systematic root cause failure analysis of the PCB can result in prevention of future issues."

Sood, Bhanu↗