SEARCH · Search NASA
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.
Statistical analysis determines probability of failure for N-1 spares. Analysis can help reduce downtime in industrial processes by determining number of spare parts that must be stockpiled. Analysis can also be inverted to decide if number of spares on hand is sufficient for lifetime needs of system.
The Reusable Solid Rocket Motor represents the largest solid rocket motor ever flown and the only human rated solid motor. Each Reusable Solid Rocket Motor (RSRM) provides approximately 3-million lb of thrust to lift the integrated Space Shuttle vehicle from the launch pad. The motors burn out approximately 2 minutes later, separate from the vehicle and are recovered and refurbished. The size of the motor and the need for high reliability were challenges. Thrust shaping, via shaping of the propellant grain, was needed to limit structural loads during ascent. The motor design evolved through several block upgrades to increase performance and to increase safety and reliability. A major redesign occurred after STS-51L with the Redesigned Solid Rocket Motor. Significant improvements in the joint sealing systems were added. Design improvements continued throughout the Program via block changes with a number of innovations including development of low temperature o-ring materials and incorporation of a unique carbon fiber rope thermal barrier material. Recovery of the motors and post flight inspection improved understanding of hardware performance, and led to key design improvements. Because of the multidecade program duration material obsolescence was addressed, and requalification of materials and vendors was sometimes needed. Thermal protection systems and ablatives were used to protect the motor cases and nozzle structures. Significant understanding of design and manufacturing features of the ablatives was developed during the program resulting in optimization of design features and processing parameters. The project advanced technology in eliminating ozone-depleting materials in manufacturing processes and the development of an asbestos-free case insulation. Manufacturing processes for the large motor components were unique and safety in the manufacturing environment was a special concern. Transportation and handling approaches were also needed for the large hardware segments. The reusable solid rocket motor achieved significant reliability via process control, ground test programs, and postflight assessment. Process control is mandatory for a solid rocket motor as an acceptance test of the delivered product is not feasible. Process control included process failure modes and effects analysis, statistical process control, witness panels, and process product integrity audits. Material controls and inspections were maintained throughout the sub tier vendors. Material fingerprinting was employed to assess any drift in delivered material properties. The RSRM maintained both full scale and sub-scale test articles. These enabled continuous improvement of design and evaluation of process control and material behavior. Additionally RSRM reliability was achieved through attention to detail in post flight assessment to observe any shift in performance. The postflight analysis and inspections provided invaluable reliability data as it enables observation of actual flight performance, most of which would not be available if the motors were not recovered. These unique challenges, features of the reusable solid rocket motor, materials and manufacturing issues, and design improvements will be discussed in the paper.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.
Room temperature tensile testing of Chemically Pure (CP) Titanium Grade 2 was conducted for as-received commercially produced sheet and following thermal exposure at 550 and 650 K for times up to 5,000 h. No significant changes in microstructure or failure mechanism were observed. A statistical analysis of the data was performed. Small statistical differences were found, but all properties were well above minimum values for CP Ti Grade 2 as defined by ASTM standards and likely would fall within normal variation of the material.
Composite tensile-failure modes, discussing failure load prediction, experimental data and statistical analysis of stress concentration effects
Lessons learned from past failures of launch vehicle developments and operations were used to create a new method to predict the probability of failure of conceptual systems. Existing methods such as Probabilistic Risk Assessments and Human Risk Assessments were considered but found to be too cumbersome for this type of system-wide application for yet-to-be-flown vehicles. The basis for this methodology were historic databases of past failures, where it was determined that various faulty human-interactions were the predominant root causes of failure rather than deficient component reliabilities evaluated through statistical analysis. This methodology contains an expert scoring part which can be used in either a qualitative or a quantitative mode. The method produces two products: a numerical score of the probability of failure and guidance to program management on critical areas in need of increased focus to improve the probability of success. In order to evaluate the effectiveness of this new method, data from a concluded vehicle program (USAF's Titan IV with the Centaur G-Prime upper stage) was used as a test case. The theoretical vs. actual probability of failure was found to be 4.46% vs. 6.67% respectively. Recommendations are made for future applications of this method to ongoing launch vehicle development programs.
Lessons learned from past failures of launch vehicle developments and operations were used to create a new method to predict the probability of failure of conceptual systems. Existing methods such as Probabilistic Risk Assessments and Human Risk Assessments were considered but found to be too cumbersome for this type of system-wide application for yet-to-be-flown vehicles. The basis for this methodology were historic databases of past failures, where it was determined that various faulty human-interactions were the predominant root causes of failure rather than deficient component reliabilities evaluated through statistical analysis. This methodology contains an expert scoring part which can be used in either a qualitative or a quantitative mode. The method produces two products: a numerical score of the probability of failure or guidance to program management on critical areas in need of increased focus to improve the probability of success. In order to evaluate the effectiveness of this new method, data from a concluded vehicle program (USAF's Titan IV with the Centaur G-Prime upper stage) was used as a test case. Although the theoretical vs. actual probability of failure was found to be in reasonable agreement (4.46% vs. 6.67% respectively) the underlying sub-root cause scoring had significant disparities attributable to significant organizational changes and acquisitions. Recommendations are made for future applications of this method to ongoing launch vehicle development programs.
Particular requirements related to the design and the operation of spacecraft have forced NASA to take a reliability approach that differs somewhat from that used in many other applications. NASA has found that some of the traditional tools of reliability engineering, such as life testing, reliability demonstration testing, maintainability analysis, and direct failure analysis, are impractical for spacecraft. In place of a statistical approach, the space agency uses an engineering approach to mission reliability. Reliability is to be obtained with the aid of three different approaches, including the application of effective design principles, the control and screening of all parts, and the testing of the entire spacecraft or its prototype for predicted capabilities. Attention is given to failure-mode analysis, the enhancement of Voyager reliability by autonomous operation, the redundancy in Shuttle design, the weeding out of bad hardware, and the preference for off-the-shelf devices.
The sources of information on which to base an evaluation of reliability or failure risk of an aerospace flight system are (1) experience from tests and flights and (2) engineering analysis. It is rarely feasible to establish high reliability at high confidence by testing aerospace systems or components. Moreover, failure prediction by conventional, deterministic methods of engineering analysis can become arbitrary and subject to serious misinterpretation when uncertain or approximate information is used to establish analysis parameter values and to calibrate the accuracy of engineering models. The limitations of testing to evaluate failure risk are discussed, and a statistical approach which incorporates both engineering analysis and testing is presented.
Statistical analysis of component reliability data for evaluating operational failures in automatic control systems
Conducting human exploration missions beyond Low Earth Orbit (LEO) will present unique challenges in the areas of supportability and maintainability. The durations of proposed missions can be relatively long and re-supply of logistics, including maintenance and repair items, will be limited or non-existent. In addition, mass and volume constraints in the transportation system will limit the total amount of logistics that can be flown along with the crew. These constraints will require that new strategies be developed with regards to how spacecraft systems are designed and maintained. NASA is currently developing Design Reference Missions (DRMs) as an initial step in defining future human missions. These DRMs establish destinations and concepts of operation for future missions, and begin to define technology and capability requirements. Because of the unique supportability challenges, historical supportability data and models are not directly applicable for establishing requirements for beyond LEO missions. However, supportability requirements could have a major impact on the development of the DRMs. The mass, volume, and crew resources required to support the mission could all be first order drivers in the design of missions, elements, and operations. Therefore, there is a need for enhanced analysis capabilities to more accurately establish mass, volume, and time requirements for supporting beyond LEO missions. Additionally, as new technologies and operations are proposed to reduce these requirements, it is necessary to have accurate tools to evaluate the efficacy of those approaches. In order to improve the analysis of supportability requirements for beyond LEO missions, the Space Missions Analysis Branch at the NASA Langley Research Center is developing the Exploration Maintainability Analysis Tool (EMAT). This tool is a probabilistic simulator that evaluates the need for repair and maintenance activities during space missions and the logistics and crew requirements to support those activities. Using a Monte Carlo approach, the tool simulates potential failures in defined systems, based on established component reliabilities, and then evaluates the capability of the crew to repair those failures given a defined store of spares and maintenance items. Statistical analysis of Monte Carlo runs provides probabilistic estimates of overall mission safety and reliability. This paper will describe the operation of the EMAT, including historical data sources used to populate the model, simulation processes, and outputs. Analysis results are provided for a candidate exploration system, including baseline estimates of required sparing mass and volume. Sensitivity analysis regarding the effectiveness of proposed strategies to reduce mass and volume requirements and improve mission reliability is included in these results.
A life cycle test of secondary electric batteries for spacecraft applications was conducted. A sample number of nickel cadmium batteries were subjected to general performance tests to determine the limit of their actual capabilities. Weaknesses discovered in cell design are reported and aid in research and development efforts toward improving the reliability of spacecraft batteries. A statistical analysis of the life cycle prediction and cause of failure versus test conditions is provided.
The service life and storage stability for several storage batteries were determined. The batteries included silver-zinc batteries, nickel-cadmium batteries, and silver-cadmium batteries. The cell performance characteristics and limitations are to be used by spacecraft power systems planners and designers. A statistical analysis of the life cycle prediction and cause of failure versus test conditions is presented.
Sensor redundancy management (SRM) requires a system which will detect failures and reconstruct avionics accordingly. A probability density function to determine false alarm rates, using an algorithmic approach was generated. Microcomputer software was developed which will print out tables of values for the cummulative probability of being in the domain of failure; system reliability; and false alarm probability, given a signal is in the domain of failure. The microcomputer software was applied to the sensor output data for various AFT1 F-16 flights and sensor parameters. Practical recommendations for further research were made.
A Lithium-Ion Cell Low-Earth-Orbit Verification Test Program is being conducted by NASA Glenn Research Center to assess the performance of lithium-ion (Li-ion) cells over a wide range of low-Earth-orbit (LEO) conditions. The data generated will be used to build an empirical model for Li-ion batteries. The goal of the modeling will be to develop a tool to predict the performance and cycle life of Li-ion batteries operating at a specified set of mission conditions. Using this tool, mission planners will be able to design operation points of the battery system while factoring in mission requirements and the expected life and performance of the batteries. Test conditions for the program were selected via a statistical design of experiments to span a range of feasible operational conditions for LEO aerospace applications. The variables under evaluation are temperature, depth-of-discharge (DOD), and end-of-charge voltage (EOCV). The baseline matrix was formed by generating combinations from a set of three values for each variable. Temperature values are 10 C, 20 C and 30 C. Depth-of-discharge values are 20%, 30% and 40%. EOCV values are 3.85 V, 3.95 V, and 4.05 V. Test conditions for individual cells may vary slightly from the baseline test matrix depending upon the cell manufacturer s recommended operating conditions. Cells from each vendor are being evaluated at each of ten sets of test conditions. Cells from four cell manufacturers are undergoing life cycle tests. Life cycling on the first sets of cells began in September 2004. These cells consist of Saft 40 ampere-hour (Ah) cells and Lith ion 30 Ah cells. These cells have achieved over 10,000 cycles each, equivalent to about 20 months in LEO. In the past year, the test program has expanded to include the evaluation of Mine Safety Appliances (MSA) 50 Ah cells and ABSL battery modules. The MSA cells will begin life cycling in October 2006. The ABSL battery modules consist of commercial Sony hard carbon 18650 lithium-ion cells configured in series and parallel combinations to create nominal 14.4 volt, 3 Ah packs (4s-2p). These modules have accumulated approximately 3000 cycles. Results on the performance of the cells and modules will be presented in this paper. The life prediction and performance model for Li-ion cells in LEO will be built by analyzing the data statistically and performing regression analysis. Cells are being cycled to failure so that differences in performance trends that occur at different stages in the life of the cell can be observed and accurately modeled. Cell testing is being performed at the Naval Surface Warfare Center in Crane, IN.
An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft.
This project is an overview and analysis of the past ten years (2013 – present) in global spaceflight, highlighting the orbital launches of countries, particularly the failures that occurred, the reasons they occurred, and a breakdown of the related statistics and background information. The analysis is conducted from a perspective of reliability and maintainability engineering and Probabilistic Risk Assessment (PRA) to formulate a quantifiable understanding of the data and how it is pertinent to Safety and Mission Assurance (SMA) in spaceflight. There is a breakdown by country or group, timelines, and number of launches. The failures over the years are categorized, all given a broad analysis of subsystem failures and details of events. A few failures are given a more in-depth analysis of root causes and failure modes determined by their unique or common nature. The data is retrieved from online, publicly available sources.