Design factors for structural reliability.
Structural design factors to achieve desired level of reliability and correct testing criteria
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.
Structural design factors to achieve desired level of reliability and correct testing criteria
Probabilistic predictions based on the Integrated Probabilistic Assessment of Composite Structures (IPACS) code are presented for the material and structural response of unnotched and notched, 1M6/3501-6 Gr/Ep laminates. Comparisons of predicted and measured modulus and strength distributions are given for unnotched unidirectional, cross-ply, and quasi-isotropic laminates. The predicted modulus distributions were found to correlate well with the test results for all three unnotched laminates. Correlations of strength distributions for the unnotched laminates are judged good for the unidirectional laminate and fair for the cross-ply laminate, whereas the strength correlation for the quasi-isotropic laminate is deficient because IPACS did not yet have a progressive failure capability. The paper also presents probabilistic and structural reliability analysis predictions for the strain concentration factor (SCF) for an open-hole, quasi-isotropic laminate subjected to longitudinal tension. A special procedure was developed to adapt IPACS for the structural reliability analysis. The reliability results show the importance of identifying the most significant random variables upon which the SCF depends, and of having accurate scatter values for these variables.
Many structural failures have occasionally been attributed to human factors in engineering design, analyses maintenance, and fabrication processes. Every facet of the engineering process is heavily governed by human factors and the degree of uncertainty associated with them. Factors such as societal, physical, professional, psychological, and many others introduce uncertainties that significantly influence the reliability of human performance. Quantifying human factors and associated uncertainties in structural reliability require: (1) identification of the fundamental factors that influence human performance, and (2) models to describe the interaction of these factors. An approach is being developed to quantify the uncertainties associated with the human performance. This approach consists of a multi factor model in conjunction with direct Monte-Carlo simulation.
Meteoroid environment and structural reliability
The goal of structural reliability analysis is to determine the probability that the structure will adequately perform its intended function when operating under the given environmental conditions. Thus, the notion of reliability admits the possibility of failure. Given the fact that many different modes of failure are usually possible, achievement of this goal is a formidable task, especially for large, complex structural systems. The traditional (deterministic) design methodology attempts to assure reliability by the application of safety factors and conservative assumptions. However, the safety factor approach lacks a quantitative basis in that the level of reliability is never known and usually results in overly conservative designs because of compounding conservatisms. Furthermore, problem parameters that control the reliability are not identified, nor their importance evaluated. A summary of recent advances in computational structural reliability assessment is presented. A significant level of activity in the research and development community was seen recently, much of which was directed towards the prediction of failure probabilities for single mode failures. The focus is to present some early results and demonstrations of advanced reliability methods applied to structural system problems. This includes structures that can fail as a result of multiple component failures (e.g., a redundant truss), or structural components that may fail due to multiple interacting failure modes (e.g., excessive deflection, resonate vibration, or creep rupture). From these results, some observations and recommendations are made with regard to future research needs.
The novel probabilistic analysis method for assessment of structural reliability presented, which combines fast-convolution with an efficient structural reliability analysis, can after identifying the most important point of a limit state proceed to establish a quadratic-performance function. It then transforms the quadratic function into a linear one, and applies fast convolution. The method is applicable to problems requiring computer-intensive structural analysis. Five illustrative examples of the method's application are given.
A new aerospace application of structural reliability techniques is presented, where the applied forces depend on many probabilistic variables. This application is the plume impingement loading of the Space Station Freedom Photovoltaic Arrays. When the space shuttle berths with Space Station Freedom it must brake and maneuver towards the berthing point using its primary jets. The jet exhaust, or plume, may cause high loads on the photovoltaic arrays. The many parameters governing this problem are highly uncertain and random. An approach, using techniques from structural reliability, as opposed to the accepted deterministic methods, is presented which assesses the probability of failure of the array mast due to plume impingement loading. A Monte Carlo simulation of the berthing approach is used to determine the probability distribution of the loading. A probability distribution is also determined for the strength of the array. Structural reliability techniques are then used to assess the array mast design. These techniques are found to be superior to the standard deterministic dynamic transient analysis, for this class of problem. The results show that the probability of failure of the current array mast design, during its 15 year life, is minute.
A new aerospace application of structural reliability techniques is presented, where the applied forces depend on many probabilistic variables. This application is the plume impingement loading of the Space Station Freedom Photovoltaic Arrays. When the space shuttle berths with Space Station Freedom it must brake and maneuver towards the berthing point using its primary jets. The jet exhaust, or plume, may cause high loads on the photovoltaic arrays. The many parameters governing this problem are highly uncertain and random. An approach, using techniques from structural reliability, as opposed to the accepted deterministic methods, is presented which assesses the probability of failure of the array mast due to plume impingement loading. A Monte Carlo simulation of the berthing approach is used to determine the probability distribution of the loading. A probability distribution is also determined for the strength of the array. Structural reliability techniques are then used to assess the array mast design. These techniques are found to be superior to the standard deterministic dynamic transient analysis, for this class of problem. The results show that the probability of failure of the current array mast design, during its 15 year life, is minute.
The influence of mesh coarseness in the structural reliability is evaluated. The objectives are to describe the alternatives and to demonstrate their effectiveness. The results show that special mapping methods can be developed by using: (1) deterministic structural responses from a fine (convergent) finite element mesh; (2) probabilistic distributions of structural responses from a coarse finite element mesh; (3) the relationship between the probabilistic structural responses from the coarse and fine finite element meshes; and (4) probabilistic mapping. The structural responses from different finite element meshes are highly correlated.
A formal method is described to quantify structural reliability and risk in the presence of a multitude of uncertainties. The method is based on the materials behavior level where primitive variables with their respective scatters are used to describe that behavior. Computational simulation is then used to propagate those uncertainties to the structural scale where reliability and risk are usually specified. A sample case is described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate that the method is mature and that it can be used for future strategic projections and planning to assure better, cheaper, faster products for competitive advantages in world markets. The results also indicate that the methods are suitable for predicting remaining life in aging or deteriorating structures.
The principal capabilities of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), an advanced computer code developed for probabilistic structural response analysis, are reviewed, and its structural reliability assessed. The code combines flexible structural modeling tools with advanced probabilistic algorithms in order to compute probabilistic structural response and resistance, component reliability and risk, and system reliability and risk. An illustrative numerical example is presented.
Use of approximations for calculating the structural failure probability under uncertainties is a necessary aspect. But efficient use of suitable approximations at different stages of reliability prediction and design makes this valuable tool practical for many large scale structures. This research addresses the modern approximation concepts and their utility in structural reliability estimation. Emphasis is placed on building higher order approximations using intervening variables to closely represent the nonlinear limit state functions. Several test problems with highly nonlinear performance functions are used to demonstrate the accuracy of the proposed method. Many of the structural problems are modeled and simulated using the finite element methods (FEM) for obtaining a detailed structural response. FEM is a computationally intensive numerical procedure with a large number of degrees of freedom. With an increase in the complexity of the structural model and the multidisciplinary nature of analyses, the number of limit states and their computation increases very significantly. The requirement of frequent repetitive analysis of the structure hinders the utility of any tool and discourages that approach from being used. One particular tool which faces this difficulty is the computation of the structural reliability under parametric uncertainties.
A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multi-factor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.
A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multifactor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.
Very few of the commercially available high performance fibers with low densities, high Young's moduli, and high tensile strengths possess all the necessary property requirements for providing either metal matrix composites (MMC) or ceramic matrix composites (CMC) with high structural reliability. These requirements are discussed in general and examples are presented of how these property guidelines are influencing fiber evaluation and improvement studies at NASA aimed at developing structurally reliable MMC and CMC for advanced gas turbine engines.
This paper describes a new approach for computing structural reliability by post-processing previously computed probabilistic results for stress and strength. The objective is to provide an accurate method whereby independent probabilistic analyses for stress and strength functions can be performed independently and combined at a later time to compute probability of failure. The method provides a capability for testing different strength measures without the need for re-computing the probabilistic stress response. The proposed approach takes full account of the basic random variables effecting both stress and strength, and the failure region in the variable space identified during separate stress/strength probabilistic analyses. A simple closed-form example and a more complex analysis of a turbine blade subject to creep rupture is used to illustrate the method.
This paper presents recent developments in efficient structural reliability analysis methods. The paper proposes an efficient, adaptive importance sampling (AIS) method that can be used to compute reliability and reliability sensitivities. The AIS approach uses a sampling density that is proportional to the joint PDF of the random variables. Starting from an initial approximate failure domain, sampling proceeds adaptively and incrementally with the goal of reaching a sampling domain that is slightly greater than the failure domain to minimize over-sampling in the safe region. Several reliability sensitivity coefficients are proposed that can be computed directly and easily from the above AIS-based failure points. These probability sensitivities can be used for identifying key random variables and for adjusting design to achieve reliability-based objectives. The proposed AIS methodology is demonstrated using a turbine blade reliability analysis problem.
Mapping methods are developed to improve the accuracy and efficiency of probabilistic structural analyses with coarse finite element meshes. The mapping methods consist of: (1) deterministic structural analyses with fine (convergent) finite element meshes, (2) probabilistic structural analyses with coarse finite element meshes, (3) the relationship between the probabilistic structural responses from the coarse and fine finite element meshes, and (4) a probabilistic mapping. The results show that the scatter of the probabilistic structural responses and structural reliability can be accurately predicted using a coarse finite element model with proper mapping methods. Therefore, large structures can be analyzed probabilistically using finite element methods.