RELIABILITY ASPECTS OF AN ENGINEERING AND PLANNING STUDY FOR LARGE SOLID ROCKET BOOSTERS
Study of techniques for reliability evaluation of solid-propellant rocket motor designs
ROCKET ENGINE↗
SEARCH · Engineering Papers
Results for “reliability study”
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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Isotope Brayton electric power system for the 500 to 2500 watt range.
An extensive study was conducted at the Lewis Research Center to evaluate an isotope Brayton electric power system for use in the 500 to 2500 W power range. The study emphasized overall system simplicity in order to reduce parasitic power losses and improve system reliability. The study included detailed parametric cycle analysis, conceptual component designs, and evaluation of system packaging. The study has resulted in the selection of a single-loop system (gas) with six major components including one rotating unit. Calculated net system efficiency varies from 23 to 28% over the power range. The use of the Pu-238 heat source being developed for the Multi-Hundred-Watt Radioisotope Thermoelectric Generator program was assumed.
Macosko, R. P.↗
A study of low-cost reliable actuators for light aircraft. Part A: Chapters 1-8
An analysis involving electro-mechanical, electro-pneumatic, and electro-hydraulic actuators was performed to study which are compatible for use in the primary and secondary flight controls of a single engine light aircraft. Actuator characteristics under investigation include cost, reliability, weight, force, volumetric requirements, power requirements, response characteristics and heat accumulation characteristics. The basic types of actuators were compared for performance characteristics in positioning a control surface model and then were mathematically evaluated in an aircraft to get the closed loop dynamic response characteristics. Conclusions were made as to the suitability of each actuator type for use in an aircraft.
Eijsink, H.↗
Design Parameters Influencing Reliability of CCGA Assembly: A Sensitivity Analysis
Area Array microelectronic packages with small pitch and large I/O counts are now widely used in microelectronics packaging. The impact of various package design and materials/process parameters on reliability has been studied through extensive literature review. Reliability of Ceramic Column Grid Array (CCGA) package assemblies has been evaluated using JPL thermal cycle test results (-50(deg)/75(deg)C, -55(deg)/100(deg)C, and -55(deg)/125(deg)C), as well as those reported by other investigators. A sensitivity analysis has been performed using the literature da to study the impact of design parameters and global/local stress conditions on assembly reliability. The applicability of various life-prediction models for CCGA design has been investigated by comparing model's predictions with the experimental thermal cycling data. Finite Element Method (FEM) analysis has been conducted to assess the state of the stress/strain in CCGA assembly under different thermal cycling, and to explain the different failure modes and locations observed in JPL test assemblies.
reliability↗
Trends in reliability modeling technology for fault tolerant systems
Developments in reliability modeling for large fault tolerant avionic computing systems are presented. Issues of state size and complexity, fault coverage, and practical computation are addressed. A two-fold developmental effort is described based on the structural and fault coverage modeling approaches. A technique which was successfully applied to an 865 state pure death stationary Markov model is presented. Of particular interest is a short computer program which executes very quickly to produce reliability results of a large state space model. This model also incorporates fault coverage states for processor, memory, and bus line replaceable units. A second structural reliability modeling scheme is aimed at solving nonstationary Markov models. This technique provides the tool required for studying the reliability of systems with nonconstant failure rates and includes intermittent/transient faults, electronic hardware which exhibits decreasing failure rates, and hydromechanical devices which typically have wearout failure mechanisms. Several aspects of fault coverage, including modeling and data measurement of intermittent/transient faults and latent faults, are elucidated and illustrated. The CARE II (computer-aided reliability estimation) coverage is presented and shortcomings to be eliminated are discussed.
Bavuso, S. J.↗
Use of Drying Technologies for Resource Recovery from Solid Wastes and Brines
Long term storage of unprocessed biological wastes and human wastes can present major health issues and a loss of potential resources. Space vehicles and planetary habitats are typically resource-scarce or resource-limited environments for long-term human habitation. To-date, most of the resources will need to be supplied from Earth, but this may not be possible for long duration human exploration. Based on present knowledge, there is only very limited in-situ resources on planetary habitats. Hence, the opportunity to "live off the land" in a planetary habitat is limited. However, if we assume that wastes generated by human explorers are viewed as resources, there is great potential to utilize and recycle them, thereby reducing the requirements for supply Earth and enabling the "live off the land" exploration scenario. Technologies used for the recovery of resources from wastes should be reliable, safe, easy to operate, fail-proof, modular, automated and preferably multifunctional in being capable of handling mixed solid and liquid wastes. For a lunar habitat, energy does not appear to be the major driving factor amongst the technologies studied. Instead, reliability appears to be more important[1] . This paper reports studies to date on drying technologies to remove water from solid wastes and brines. Experimental performance data obtained for recovery water from wastes and brine are presented. Simplicity of operation of hardware and energy efficiency are discussed. Some improvements and modifications to hardware were performed. Hopefully, this information will assist in future efforts in the "downselection" of technologies for recovery of water and resources from solid wastes and brines.
Wignarajah, Kanapathipillai↗
Reliability growth during a development testing program
Binomial and trinomial mathematical models for reliability growth studies - statistical analysis of system failures
MATHEMATICAL MODEL↗
Understanding software faults and their role in software reliability modeling
This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.
Munson, John C.↗
Space Tug Point Design Study. Volume 3: Design Definition. Part 2: Insulation, Meteoroid Protection, Structures, Mass Properties, GSE, Reliability and Safety
A study was conducted to determine the configuration and performance of a space tug. Detailed descriptions of the insulation, meteoroid protection, primary structure, and ground support equipment are presented. Technical assessments leading to the concept selection are analyzed. The tug mass properties, reliability, and safety assessments are included.
Source record↗
Optimum design of spacecraft radiators for large capacity or long duration mission applications
Results of trade studies comparing optimum pumped fluid and hybrid heat pipe/pumped fluid radiator designs are presented. Design variables considered in the system optimization analyses include transport fluid properties and Reynolds number, pump loop component reliabilities, micrometeoroid protection, heat pipe spacing, manifolding and flow routing in pumped fluid designs, transport tubing spacing and diameters, and radiator fin parameters. Criteria for evaluating candidate radiator system designs include mass, radiator area, and overall system reliability. The studies show advantages in constructing large systems from small independent subsystems with oversizing to account for subsystem failures. The optimum size and survivability of the subsystems are identified as a function of the radiator construction, the system heat rejection requirements, the mission duration, and the system reliability requirements.
Leach, J. W.↗
Integrating Reliability Analysis with a Performance Tool
A large number of commercial simulation tools support performance oriented studies of complex computer and communication systems. Reliability of these systems, when desired, must be obtained by remodeling the system in a different tool. This has obvious drawbacks: (1) substantial extra effort is required to create the reliability model; (2) through modeling error the reliability model may not reflect precisely the same system as the performance model; (3) as the performance model evolves one must continuously reevaluate the validity of assumptions made in that model. In this paper we describe an approach, and a tool that implements this approach, for integrating a reliability analysis engine into a production quality simulation based performance modeling tool, and for modeling within such an integrated tool. The integrated tool allows one to use the same modeling formalisms to conduct both performance and reliability studies. We describe how the reliability analysis engine is integrated into the performance tool, describe the extensions made to the performance tool to support the reliability analysis, and consider the tool's performance.
Nicol, David M.↗
Investigation of spectral analysis techniques for randomly sampled velocimetry data
It is well known that velocimetry (LV) generates individual realization velocity data that are randomly or unevenly sampled in time. Spectral analysis of such data to obtain the turbulence spectra, and hence turbulence scales information, requires special techniques. The 'slotting' technique of Mayo et al, also described by Roberts and Ajmani, and the 'Direct Transform' method of Gaster and Roberts are well known in the LV community. The slotting technique is faster than the direct transform method in computation. There are practical limitations, however, as to how a high frequency and accurate estimate can be made for a given mean sampling rate. These high frequency estimates are important in obtaining the microscale information of turbulence structure. It was found from previous studies that reliable spectral estimates can be made up to about the mean sampling frequency (mean data rate) or less. If the data were evenly samples, the frequency range would be half the sampling frequency (i.e. up to Nyquist frequency); otherwise, aliasing problem would occur. The mean data rate and the sample size (total number of points) basically limit the frequency range. Also, there are large variabilities or errors associated with the high frequency estimates from randomly sampled signals. Roberts and Ajmani proposed certain pre-filtering techniques to reduce these variabilities, but at the cost of low frequency estimates. The prefiltering acts as a high-pass filter. Further, Shapiro and Silverman showed theoretically that, for Poisson sampled signals, it is possible to obtain alias-free spectral estimates far beyond the mean sampling frequency. But the question is, how far? During his tenure under 1993 NASA-ASEE Summer Faculty Fellowship Program, the author investigated from his studies on the spectral analysis techniques for randomly sampled signals that the spectral estimates can be enhanced or improved up to about 4-5 times the mean sampling frequency by using a suitable prefiltering technique. But, this increased bandwidth comes at the cost of the lower frequency estimates. The studies further showed that large data sets of the order of 100,000 points, or more, high data rates, and Poisson sampling are very crucial for obtaining reliable spectral estimates from randomly sampled data, such as LV data. Some of the results of the current study are presented.
Sree, Dave↗
Trends in reliability modeling technology for fault tolerant systems
Reliability modeling for fault tolerant avionic computing systems was developed. The modeling of large systems involving issues of state size and complexity, fault coverage, and practical computation was discussed. A novel technique which provides the tool for studying the reliability of systems with nonconstant failure rates is presented. The fault latency which may provide a method of obtaining vital latent fault data is measured.
Bavuso, S. J.↗
Structural Reliability Using Probability Density Estimation Methods Within NESSUS
A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.
Chamis, Chrisos C.↗
A study of the longevity and operational reliability of Goddard Spacecraft, 1960-1980
Compiled data regarding the design lives and lifetimes actually achieved by 104 orbiting satellites launched by the Goddard Spaceflight Center between the years 1960 and 1980 is analyzed. Historical trends over the entire 21 year period are reviewed, and the more recent data is subjected to an examination of several key parameters. An empirical reliability function is derived, and compared with various mathematical models. Data from related studies is also discussed. The results provide insight into the reliability history of Goddard spacecraft an guidance for estimating the reliability of future programs.
Shockey, E. F.↗
Stepwise Iterative Fourier Transform: The SIFT
A program, designed specifically to study the respective effects of some common data problems on results obtained through stepwise iterative Fourier transformation of synthetic data with known waveform composition, was outlined. Included in this group were the problems of gaps in the data, different time-series lengths, periodic but nonsinusoidal waveforms, and noisy (low signal-to-noise) data. Results on sinusoidal data were also compared with results obtained on narrow band noise with similar characteristics. The findings showed that the analytic procedure under study can reliably reduce data in the nature of (1) sinusoids in noise, (2) asymmetric but periodic waves in noise, and (3) sinusoids in noise with substantial gaps in the data. The program was also able to analyze narrow-band noise well, but with increased interpretational problems. The procedure was shown to be a powerful technique for analysis of periodicities, in comparison with classical spectrum analysis techniques. However, informed use of the stepwise procedure nevertheless requires some background of knowledge concerning characteristics of the biological processes under study.
Benignus, V. A.↗
Models of Wake-Vortex Spreading Mechanisms and Their Estimated Uncertainties
One of the primary constraints on the capacity of the nation's air transportation system is the landing capacity at its busiest airports. Many airports with nearly-simultaneous operations on closely-spaced parallel runways (i.e., as close as 750 ft (246m)) suffer a severe decrease in runway acceptance rate when weather conditions do not allow full utilization. The objective of a research program at NASA Ames Research Center is to develop the technologies needed for traffic management in the airport environment so that operations now allowed on closely-spaced parallel runways under Visual Meteorological Conditions can also be carried out under Instrument Meteorological Conditions. As part of this overall research objective, the study reported here has developed improved models for the various aerodynamic mechanisms that spread and transport wake vortices. The purpose of the study is to continue the development of relationships that increase the accuracy of estimates for the along-trail separation distances available before the vortex wake of a leading aircraft intrudes into the airspace of a following aircraft. Details of the models used and their uncertainties are presented in the appendices to the paper. Suggestions are made as to the theoretical and experimental research needed to increase the accuracy of and confidence level in the models presented and instrumentation required or more precise estimates of the motion and spread of vortex wakes. The improved wake models indicate that, if the following aircraft is upwind of the leading aircraft, the vortex wakes of the leading aircraft will not intrude into the airspace of the following aircraft for about 7s (based on pessimistic assumptions) for most atmospheric conditions. The wake-spreading models also indicate that longer time intervals before wake intrusion are available when atmospheric turbulence levels are mild or moderate. However, if the estimates for those time intervals are to be reliable, further study is necessary to develop the instrumentation and procedures needed to accurately define when the more benign atmospheric conditions exist.
Rossow, Vernon J.↗