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Interaction equations were proposed earlier to predict failure in Albemet 162 brazed joints. Present study demonstrates that the same interaction equations can be used for lower bound estimate of the failure criterion in 304 stainless steel joints brazed with silver-based filler metals as well as for construction of the Failure Assessment Diagrams (FAD).
The National Transonic Facility has three centrifugal multi-stage pumps to supply liquid nitrogen to the wind tunnel. Pump reliability is critical to facility operation and test capability. A highly desirable goal is to be able to detect a pump rotating component problem as early as possible during normal operation and avoid serious damage to other pump components. If a problem is detected before serious damage occurs, the repair cost and downtime could be reduced significantly. A neural network-based tool was developed for monitoring pump performance and aiding in predicting pump failure. Once trained, neural networks can rapidly process many combinations of input values other than those used for training to approximate previously unknown output values. This neural network was applied to establish relationships among the critical frequencies and aid in predicting failures. Training pairs were developed from frequency scans from typical tunnel operations. After training, various combinations of critical pump frequencies were propagated through the neural network. The approximated output was used to create a contour plot depicting the relationships of the input frequencies to the output pump frequency.
Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.
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.
Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I project
Isotopic taggants are being studied to aid in the provenance assessment of nuclear materials. However, these taggants must be selected such that they do not adversely affect fuel performance during normal operation or accident scenarios. Taggants are known to affect the fuel’s grain size and porosity. In the work described in this paper, the BISON fuel performance code was used to assess the potential effects of taggants (i.e., grain size and porosity) on fuel rod behavior and cladding failure during a high-burnup, large-break loss-of-coolant accident. Here, 281 individual fuel rods from the same reactor core were modeled for a sensitivity study, a parametric study, and uncertainty quantification. The cladding failure predictions often exhibited stochastic behavior. After additional study, it was found that the cladding failure model is highly sensitive to residual error inherent to numerical approximation solvers. Some strategies to mitigate this sensitivity are discussed. The study found no relationship between known taggant effects and cladding failure status. However, taggants were found to affect the time and location of failure in certain rods. In conclusion, future work to continue investigating and validating these findings is briefly discussed.
A probabilistic high cycle fatigue (HCF) failure analysis of a welded duct in a rocket engine of the Space Shuttle main engine class is described. A state-of-the-art HCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic stress/life model is used for material characterization, and a composite stress history is generated for accurately deriving the stress cycles for the fatigue-damage calculations. The HCF failure model expresses fatigue life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A series of HCF failure life analyses were performed to study the impact of a fixed parameter and to assess the importance of each stochastic input parameter through marginal analyses.
A probabilistic Low Cycle Fatigue (LCF) failure analysis of a candidate turbine disk for use in a turbopump of a rocket engine of the Space Shuttle Main Engine class is described. A state-of-the-art LCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic Stress/Life (S/N) model was used for materials characterization. The LCF failure model expresses fatigue life as a function of stochastic parameters including environmental parameters, loads, material properties, structural parameters, and model specification errors. The rationale for the particular characterization of each stochastic input parameter is described. The results and interpretation of the failure analysis are given.
Data from sufficiently well-instrumented, short-duration experiments at AEDC/HPDE, Reynolds Metal Co., and Hercules, Inc., are compared to analyses with multidimensional and time-dependent simulations with the STD/MHD computer codes. These analyses reveal detailed features of major transient events, severe loss mechanisms, and anomalous MHD behavior. In particular, these analyses predicted higher-than-design voltage drops, Hall voltage overshoots, and asymmetric voltage drops before the experimental data were available. The predictions obtained with these analyses are in excellent agreement with the experimental data and the failure predictions are consistent with the experiments. The design of large, high-interaction or advanced MHD experiments will require application of sophisticated, detailed and comprehensive computational procedures in order to account for the critical mechanisms which led to the observed behavior in these experiments.
While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.
While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.
This report documents a validation study on the standard Open Hole Compression (OHC) laminate test specimen conducted as part of the NASA Advanced Composites Project (ACP). Tests were conducted on OHC specimens with hard, quasi-isotropic, and soft layups using digital image correlation and X-Ray computed tomography to capture the structural response and damage evolution. Progressive damage models were constructed for use with the CompDam continuum damage mechanics code following the best practices established during the ACP. Detailed interrogation of the analysis results and comparison with experimental measurements provide a basis for assessing the capability of the modeling approach for OHC. The structural response is found to be captured well, with strengths predicted within 3\% of the experimental values for hard and quasi-isotropic laminates. In the soft laminate, the model predicts failure to be more brittle than the nonlinear, ductile response that was measured. Damage states extracted from the models at the same load level as test measurements are overlaid to show directly the similarities and differences between test and analysis results. Studying the damage evolution predicted by the analysis reveals that the failure process is a competition between fiber damage and delamination/sub-laminate buckling, with fiber damage dominating the collapse in the hard laminate and sub-laminate buckling governing in the soft laminate. Finally, a series of parametric studies varying numerical solution parameters (mesh size, mass scaling) and physical properties (fiber direction compressive strength and toughness) reveal sensitivities and deficiencies of the model. To the authors' knowledge, this study is the first for OHC specimens to include detailed evaluation of damage mode interactions, direct overlay of predicted and measured damage states, and sensitivity of the predicted results to difficult-to-measure fiber direction material properties.
A validated semiempirical design procedure and fatigue data were developed for curved, stiffened composite panels operating in the postbuckled regime under the action of combined compression and shear loading. A previously developed design methodology for composite panels under pure shear or pure compression loading was used as the starting point for the program. Initially, the well established interaction rules for metal panels were adopted to predict buckling under combined loading. Test data were then developed to verify these rules and suggest modifications where necessary. Postbuckling failure envelopes were developed by accounting for the failure modes possible under shear loading only, and under pure compression loading. Static failure predictions under combined loading were based on test verified interaction criteria. Fatigue tests were conducted under combined loading to determine strength degradation and the possible failure modes.
The ability of tri-structural isotropic (TRISO) fuel to contain fission products is largely dictated by the quality of the manufacturing process, since most of the fission product release is expected to occur due to coating layer failure in a small number of particles containing defects. The Bison fuel performance code has capabilities to predict failure in individual particles, accounting for the presence of defects, and to apply statistical analysis methods to compute the probability of failure in a set of fuel particles. Bison has recently undergone significant development both to improve its physical representations of fuel particle behavior and to improve the efficiency of its statistical failure calculations. Physical model improvements include new capabilities to account for the pressure generated by fission gases on inner pyrolytic carbon (IPyC) crack surfaces and to use local material coordinate orientation to accurately incorporate the anisotropy in the material properties in aspherical particles. To improve statistical modeling efficiency, a direct integration approach which involves directly integrating the failure probability function associated with statistically varying parameters has been developed. The direct integration approach is much more efficient than the Monte Carlo (MC) schemes commonly employed, and allows Bison to directly run high-dimensional fuel performance models, which improves the accuracy of failure probability calculations. Finally, a set of benchmark problems is considered here to compare the MC and direct integration approaches, and a statistical failure analysis of compacts in the Advanced Gas Reactor (AGR)-2 experiments is performed using the direct integration approach.
Non-linear analysis methods were adapted and incorporated in a finite element based DIAL code. These methods are necessary to evaluate the global response of a stiffened structure under combined in-plane and out-of-plane loading. These methods include the Arc Length method and target point analysis procedure. A new interface material model was implemented that can model elastic-plastic behavior of the bond adhesive. Direct application of this method is in skin/stiffener interface failure assessment. Addition of the AML (angle minus longitudinal or load) failure procedure and Hasin's failure criteria provides added capability in the failure predictions. Interactive Stiffened Panel Analysis modules were developed as interactive pre-and post-processors. Each module provides the means of performing self-initiated finite elements based analysis of primary structures such as a flat or curved stiffened panel; a corrugated flat sandwich panel; and a curved geodesic fuselage panel. This module brings finite element analysis into the design of composite structures without the requirement for the user to know much about the techniques and procedures needed to actually perform a finite element analysis from scratch. An interactive finite element code was developed to predict bolted joint strength considering material and geometrical non-linearity. The developed method conducts an ultimate strength failure analysis using a set of material degradation models.
An investigation of the damage tolerance and damage arrest characteristics of internally-pressurized graphite/epoxy tape cylinders with axial notches was conducted. An existing failure prediction methodology, developed and verified for quasi-isotropic graphite/epoxy fabric cylinders, was investigated for applicability to general tape layups. In addition, the effect of external circumferential stiffening bands on the direction of fracture path propagation and possible damage arrest was examined. Quasi-isotropic (90/0/plus or minus 45)s and structurally anisotropic (plus or minus 45/0)s and (plus or minus 45/90)s coupons and cylinders were constructed from AS4/3501-6 graphite/epoxy tape. Notched and unnotched coupons were tested in tension and the data correlated using the equation of Mar and Lin. Cylinders with through-thickness axial slits were pressurized to failure achieving a far-field two-to-one biaxial stress state. Experimental failure pressures of the (90/0/plus or minus 45)s cylinders agreed with predicted values for all cases but the specimen with the smallest slit. However, the failure pressures of the structurally anisotropic cylinders, (plus or minus 45/0)s and (plus or minus 45/90)s, were above the values predicted utilizing the predictive methodology in all cases. Possible factors neglected by the predictive methodology include structural coupling in the laminates and axial loading of the cylindrical specimens. Furthermore, applicability of the predictive methodology depends on the similarity of initial fracture modes in the coupon specimens and the cylinder specimens of the same laminate type. The existence of splitting which may be exacerbated by the axial loading in the cylinders, shows that this condition is not always met. The circumferential stiffeners were generally able to redirect fracture propagation from longitudinal to circumferential. A quantitative assessment for stiffener effectiveness in containing the fracture, based on cylinder radius, slit size, and bending stiffnesses of the laminates, is proposed.
The NASA Composite Technology for Exploration (CTE) Project is developing and demonstrating critical composite technologies with a focus on composite bonded joints; incorporating materials, design/analysis, manufacturing, and tests that utilize NASA’s expertise and capabilities. The project has goals of advancing composite technologies and providing lightweight structures to support future NASA exploration missions. In particular, the CTE project will demonstrate weight-saving, performance-enhancing composite bonded joint technology for Space Launch System (SLS)-scale composite hardware. Advancements from the CTE project may be incorporated as future block upgrades for SLS structural components. This paper discusses the details of the development of a composite sandwich bonded longitudinal joint for a generic space launch vehicle structure called the CTE Point Design. The paper includes details of the design, analysis, materials, manufacturing, and testing of sub-element joint test articles to test the capability of the joint design. The test results show that the composite longitudinal bonded joint design significantly exceeds the design loads with a 2.0 factor of safety. Analysis pre-test failure predictions for all sub-element bonded joint test coupons were all within 10% of the average test coupon failure load. This testing and analysis provides confidence in the potential use of composite bonded joints for future launch vehicle structures.
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 engineering analysis 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 engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis 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. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.