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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 NTRS2002

Failure Mode Identification Through Clustering Analysis

Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

Arunajadai, Srikesh G.↗
NASA NTRS2006

Probabilistic Risk Assessment (PRA): A Practical and Cost Effective Approach

The Lunar Reconnaissance Orbiter (LRO) is the first mission of the Robotic Lunar Exploration Program (RLEP), a space exploration venture to the Moon, Mars and beyond. The LRO mission includes spacecraft developed by NASA Goddard Space Flight Center (GSFC) and seven instruments built by GSFC, Russia, and contractors across the nation. LRO is defined as a measurement mission, not a science mission. It emphasizes the overall objectives of obtaining data to facilitate returning mankind safely to the Moon in preparation for an eventual manned mission to Mars. As the first mission in response to the President's commitment of the journey of exploring the solar system and beyond: returning to the Moon in the next decade, then venturing further into the solar system, ultimately sending humans to Mars and beyond, LRO has high-visibility to the public but limited resources and a tight schedule. This paper demonstrates how NASA's Lunar Reconnaissance Orbiter Mission project office incorporated reliability analyses in assessing risks and performing design tradeoffs to ensure mission success. Risk assessment is performed using NASA Procedural Requirements (NPR) 8705.5 - Probabilistic Risk Assessment (PRA) Procedures for NASA Programs and Projects to formulate probabilistic risk assessment (PRA). As required, a limited scope PRA is being performed for the LRO project. The PRA is used to optimize the mission design within mandated budget, manpower, and schedule constraints. The technique that LRO project office uses to perform PRA relies on the application of a component failure database to quantify the potential mission success risks. To ensure mission success in an efficient manner, low cost and tight schedule, the traditional reliability analyses, such as reliability predictions, Failure Modes and Effects Analysis (FMEA), and Fault Tree Analysis (FTA), are used to perform PRA for the large system of LRO with more than 14,000 piece parts and over 120 purchased or contractor built components.

Lee, Lydia L.↗
NASA NTRS2007

Abort Flight Test Project Overview

A general overview of the Orion abort flight test is presented. The contents include: 1) Abort Flight Test Project Overview; 2) DFRC Exploration Mission Directorate; 3) Abort Flight Test; 4) Flight Test Configurations; 5) Flight Test Vehicle Engineering Office; 6) DFRC FTA Scope; 7) Flight Test Operations; 8) DFRC Ops Support; 9) Launch Facilities; and 10) Scope of Launch Abort Flight Test

Sitz, Joel↗
NASA NTRS2009

Parts and Components Reliability Assessment: A Cost Effective Approach

System reliability assessment is a methodology which incorporates reliability analyses performed at parts and components level such as Reliability Prediction, Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to assess risks, perform design tradeoffs, and therefore, to ensure effective productivity and/or mission success. The system reliability is used to optimize the product design to accommodate today?s mandated budget, manpower, and schedule constraints. Stand ard based reliability assessment is an effective approach consisting of reliability predictions together with other reliability analyses for electronic, electrical, and electro-mechanical (EEE) complex parts and components of large systems based on failure rate estimates published by the United States (U.S.) military or commercial standards and handbooks. Many of these standards are globally accepted and recognized. The reliability assessment is especially useful during the initial stages when the system design is still in the development and hard failure data is not yet available or manufacturers are not contractually obliged by their customers to publish the reliability estimates/predictions for their parts and components. This paper presents a methodology to assess system reliability using parts and components reliability estimates to ensure effective productivity and/or mission success in an efficient manner, low cost, and tight schedule.

Lee, Lydia↗
NASA NTRS2010

Orion Pad Abort 1 GN and C Design and Development

The first flight test of the Orion Abort Flight Test project is scheduled to launch in Spring 2010. This flight test is known as Pad Abort 1 (PA-1) and it is intended to accomplish a series of flight test objectives, including demonstrating the capability of the Launch Abort System (LAS) to propel the Crew Module (CM) to a safe distance from a launch vehicle during a pad abort. The PA-1 Flight Test Article (FTA) is actively controlled by a guidance, navigation, and control (GN&C) system for much of its flight. The purpose of this paper is to describe the design, development, and analysis of the PA-1 GN&C system. A description of the technical solutions that were developed to meet the challenge of satisfying many competing requirements is presented. A historical perspective of how the Orion LAV compares to the Apollo Launch Escape Vehicle (LEV) design will also be included.

Medina, Edgar A.↗
NASA NTRS2010

Orion FSW V and V and Kedalion Engineering Lab Insight

NASA, along with its prime Orion contractor and its subcontractor s are adapting an avionics system paradigm borrowed from the manned commercial aircraft industry for use in manned space flight systems. Integrated Modular Avionics (IMA) techniques have been proven as a robust avionics solution for manned commercial aircraft (B737/777/787, MD 10/90). This presentation will outline current approaches to adapt IMA, along with its heritage FSW V&V paradigms, into NASA's manned space flight program for Orion. NASA's Kedalion engineering analysis lab is on the forefront of validating many of these contemporary IMA based techniques. Kedalion has already validated many of the proposed Orion FSW V&V paradigms using Orion's precursory Flight Test Article (FTA) Pad Abort 1 (PA-1) program. The Kedalion lab will evolve its architectures, tools, and techniques in parallel with the evolving Orion program.

Mangieri, Mark L.↗
NASA NTRS2011

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment.

Stamatelatos,Michael↗
NASA NTRS2014

Structural Characterization of Lateral-grown 6H-SiC am-plane Seed Crystals by Hot Wall CVD Epitaxy

The performance of commercially available silicon carbide (SiC) power devices is limited due to inherently high density of screw dislocations (SD), which are necessary for maintaining polytype during boule growth and commercially viable growth rates. The NASA Glenn Research Center (GRC) has recently proposed a new bulk growth process based on axial fiber growth (parallel to the c-axis) followed by lateral expansion (perpendicular to the c-axis) for producing multi-faceted m-plane SiC boules that can potentially produce wafers with as few as one SD per wafer. In order to implement this novel growth technique, the lateral homoepitaxial growth expansion of a SiC fiber without introducing a significant number of additional defects is critical. Lateral expansion is being investigated by hot wall chemical vapor deposition (HWCVD) growth of 6H-SiC am-plane seed crystals (0.8mm x 0.5mm x 15mm) designed to replicate axially grown SiC single crystal fibers. The post-growth crystals exhibit hexagonal morphology with approximately 1500 m (1.5 mm) of total lateral expansion. Preliminary analysis by synchrotron white beam x-ray topography (SWBXT) confirms that the growth was homoepitaxial, matching the polytype of the respective underlying region of the seed crystal. Axial and transverse sections from the as grown crystal samples were characterized in detail by a combination of SWBXT, transmission electron microscopy (TEM) and Raman spectroscopy to map defect types and distribution. X-ray diffraction analysis indicates the seed crystal contained stacking disorders and this appears to have been reproduced in the lateral growth sections. Analysis of the relative intensity for folded transverse acoustic (FTA) and optical (FTO) modes on the Raman spectra indicate the existence of stacking faults. Further, the density of stacking faults is higher in the seed than in the grown crystal. Bundles of dislocations are observed propagating from the seed in m-axis lateral directions. Contrast extinction analysis of these dislocation lines reveals they are edge type basal plane dislocations that track the growth direction. Polytype phase transition and stacking faults were observed by high-resolution TEM (HRTEM), in agreement with SWBXT and Raman scattering.

Single Crystal↗
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 NTRS2018

NC Space Grant Report

During the summer of 2018 I supported the Safety & Mission Assurance Directorate (SMA) and Operations Support Division (QA-20) at Stennis Space Center. The mission of the SMA team is to prove safety, risk, reliability, independent assessments, configuration management and quality assurance guidance, and services for all NASA Stennis Space Center (SSC) programs, facilities, and supporting infrastructure. The office actively participates and contributes to the Agency-level Safety & Mission Assurance (S&MA) effort. Over the course of the Summer I participated in three projects. Two of them were focused around Fault Tree Analysis (FTA) and the third focused on relief valves for their E-1 engine test stand.

Torres, David↗
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

Failure Analysis and Products in a Model-Based Environment

The work presented in this paper describes an approach, including a methodology and tools, which allows system engineers to capture failure-related information in a model and generate automatically key failure analysis products: the Failure Modes, Effects and Criticality Analysis (FMECA) and the Fault Tree Analysis (FTA). The work has been developed by Tietronix Software, Inc. and the NASA’s Jet Propulsion Laboratory (JPL), and the resulting auto-generated artifacts shown in this paper demonstrate the ability to obtain powerful reliability and fault management products in a model-based environment.

Castet, Jean-Francois↗
NASA NTRS2019

Model Based Engineering for Software Assurance

NASA's successful development of next generation space vehicles, habitats, and robotic systems will require reliable hardware and software systems. The aim of this initiative is to develop modeling methodology and tools to support Model-Based Systems Engineering (MBSE) for software assurance and reliability analysis. This effort expands the Unified Modeling Language (UML) software design models to include fault data for the extraction of Failure Modes and Effects Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) for software. We explored different modeling approaches to integrate the UML software design models with the Systems Modeling Language (SysML) system models to generate an integrated model and reliability tools that take into account software and hardware interfaces.The benefits of this concept directly affect the safety community with quick turnarounds to produce software assurance and reliability analysis artifacts and the ability to visualize failure effects, both hardware and software. The result is enhanced system design integrity and early identification of system risks. This initiative will enable software assurance activities early in the system design lifecycle, facilitating the discovery of design weaknesses and enhancing the capability to produce safe, hazard-free systems

Wang, Lui↗
NASA NTRS2024

Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids

This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.

Leila Chebbo↗
DOE OSTI2024

An Action Plan for Rail Energy and Emissions Innovation

The Action Plan for Rail Energy and Emissions Innovation proposes actions to reduce and nearly eliminate emissions in the U.S. rail sector, in line with the U.S. economy-wide goal of net-zero greenhouse gas (GHG) emissions by 2050. It also proposes actions to leverage the rail system to reduce emissions from other modes. The national goal of achieving a zero-emission freight system by 2050 draws our attention to the fact that freight transport cannot be addressed simply mode by mode, but it should instead be treated as an interdependent system. This is especially true when pursuing decarbonization. This action plan presents how both rail transport and decarbonization intersect with our national transportation decarbonization blueprint, the decarbonization of the freight system, and national transmission goals. The intended audience of this report is the stakeholders who will advance rail decarbonization in a just and economical way by propelling the suite of actions listed here. This includes government at all levels, rail companies, locomotive manufacturers, labor unions, Amtrak, and more.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2015

Infusing Reliability Techniques into Software Safety Analysis

Software safety analysis for a large software intensive system is always a challenge. Software safety practitioners need to ensure that software related hazards are completely identified, controlled, and tracked. This paper discusses in detail how to incorporate the traditional reliability techniques into the entire software safety analysis process. In addition, this paper addresses how information can be effectively shared between the various practitioners involved in the software safety analyses. The author has successfully applied the approach to several aerospace applications. Examples are provided to illustrate the key steps of the proposed approach.

FTA↗
NASA NTRS2021

Architectural Modeling and Analysis for Safety Engineering

Model-based development tools are increasingly being used for system-level development of safety-critical systems. Architectural and behavioral models provide important information that can be leveraged to improve the system safety analysis process. Model-based design artifacts produced in early stage development activities can be used to perform system safety analysis, reducing costs and providing accurate results throughout the system life-cycle. In this report we describe an extension to the Architecture Analysis and Design Language (AADL) that supports modeling of system behavior under failure conditions. This Safety Annex enables the independent modeling of component failures and allows safety engineers to weave various types of fault behavior into the nominal system model. The accompanying tool support uses model checking to propagate errors from their source to their effect on safety properties without the need to add separate propagation specifications. The tool also captures all minimal set of fault combinations that can cause violation of the safety properties, that can be compared to qualitative and quantitative objectives as part of the safety assessment process. We describe the Safety Annex, illustrate its use with a representative example, and discuss and demonstrate the tool support enabling an analyst to investigate the system behavior under failure conditions.

FTA↗