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The critical role played by adhesive bonds in the fracture and failure of composites and other bonded materials is well known. A good knowledge of the mechanical properties of these adhesion joints is a prerequisite to reliable design and reliable prediction of the performance of these bonded structures.
A stochastic design optimization methodology (SDO) has been developed to design components of an airframe structure that can be made of metallic and composite materials. The design is obtained as a function of the risk level, or reliability, p. The design method treats uncertainties in load, strength, and material properties as distribution functions, which are defined with mean values and standard deviations. A design constraint or a failure mode is specified as a function of reliability p. Solution to stochastic optimization yields the weight of a structure as a function of reliability p. Optimum weight versus reliability p traced out an inverted-S-shaped graph. The center of the inverted-S graph corresponded to 50 percent (p = 0.5) probability of success. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure that corresponds to unity for reliability p (or p = 1). Weight can be reduced to a small value for the most failure-prone design with a reliability that approaches zero (p = 0). Reliability can be changed for different components of an airframe structure. For example, the landing gear can be designed for a very high reliability, whereas it can be reduced to a small extent for a raked wingtip. The SDO capability is obtained by combining three codes: (1) The MSC/Nastran code was the deterministic analysis tool, (2) The fast probabilistic integrator, or the FPI module of the NESSUS software, was the probabilistic calculator, and (3) NASA Glenn Research Center s optimization testbed CometBoards became the optimizer. The SDO capability requires a finite element structural model, a material model, a load model, and a design model. The stochastic optimization concept is illustrated considering an academic example and a real-life raked wingtip structure of the Boeing 767-400 extended range airliner made of metallic and composite materials.
This paper investigates the use of proof (or acceptance) test data during the reliability based design optimization of structural components. It is assumed that every component will be proof tested and that the component will only enter into service if it passes the proof test. The goal is to reduce the component weight, while maintaining high reliability, by exploiting the proof test results during the design process. The proposed procedure results in the simultaneous design of the structural component and the proof test itself and provides the designer with direct control over the probability of failing the proof test. The procedure is illustrated using two analytical example problems and the results indicate that significant weight savings are possible when exploiting the proof test results during the design process.
The reliable design of magnetically insulated transmission lines (MITLs) for very high current pulsed power machines must be accomplished in the future by utilizing a variety of sophisticated modeling tools. The complexity of the models required is high and the number of sub-models and approximations large. The potential for significant analyst error using a single tool is large, with possible reliability issues associated with the plasma modeling tools themselves or the chosen approach by the analyst to solve a given problem. Here, we report on a software infrastructure design that provides a workable framework for building self-consistent models and constraining feedback to limit analyst error. The framework and associated tools aid the development of physical intuition, the development of increasingly sophisticated models, and the comparison of performance results. The work lays the computational foundation for designing state-of-the-art pulsed-power experiments. The design and useful features of this environment are described. We discuss the utility of the Git source code management system and a GitLab interface for use in project management that extends beyond software development tasks.
Theory and techniques applicable to design, analysis, and fault diagnosis of reliable spacecraft data systems
Structural design factors to achieve desired level of reliability and correct testing criteria
Reliable, secure access to energy is a major focus for national security efforts. One potential route to such energy is through fusion reactions in inertial confinement fusion (ICF) experiments. Such experiments are carried out at facilities such as the National Ignition Facility (NIF) in Livermore, California, where high powered lasers are used to compress a DT fuel-containing target to the necessary high temperature, high pressure conditions. These experiments are limited in number, which creates a heavy dependence on high fidelity predictive physics simulations and analysis performed “pre shot,” or before the experiment occurs. Many of these simulations in higher dimensions (2D and 3D) are computationally expensive, so finding optimal simulation-based designs presents its own challenges. In this work, we present our multi-fidelity Bayesian optimization with Gaussian processes (GPs) for ICF double shell targets, where a 1D surrogate model is used to help find a 2D surrogate model, enabling us to find optimal targets in the higher fidelity (2D), while saving computational cost.
The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program intended for sending crew and cargo to the international Space Station (ISS), to the moon, and beyond. This program is called Constellation. As part of the Constellation program, NASA is developing new launch vehicles aimed at significantly increase safety and reliability, reduce the cost of accessing space, and provide a growth path for manned space exploration. Achieving these goals requires a rigorous process that addresses reliability, safety, and cost upfront and throughout all the phases of the life cycle of the program. This paper discusses the "Design for Reliability and Safety" approach for the NASA new launch vehicles, the ARES I and ARES V. Specifically, the paper addresses the use of an integrated probabilistic functional analysis to support the design analysis cycle and a probabilistic risk assessment (PRA) to support the preliminary design and beyond.
Mariner Mars 1964 spacecraft power system design for high reliability utilizing redundant elements and failure sensing circuits
The division's philosophy for designed-in reliability and a comparison of reliability programs for space, manned military aircraft, and commercial aircraft, are presented. Topics include: the reliability interface with design and production; the concept phase through final proposal; the design, development, test and evaluation phase; the production phase; and the commonality among space, military, and commercial avionics.
A brief description of the complex fatigue machines used in the test program is presented. The data generated from these machines are given and discussed. Two methods of obtaining strength distributions from the data are also discussed. Then follows a discussion of the construction of statistical fatigue diagrams and their use in designing by reliability. Finally, some of the problems encountered in the test equipment and a corrective modification are presented.
This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.
Products made from advanced ceramics show great promise for revolutionizing aerospace and terrestrial propulsion, and power generation. However, ceramic components are difficult to design because brittle materials in general have widely varying strength values. The CAPES/Life software eases this task by providing a tool to optimize the design and manufacture of brittle material components using probabilistic reliability analysis techniques. Probabilistic component design involves predicting the probability of failure for a thermomechanically loaded component from specimen rupture data. Typically, these experiments are performed using many simple geometry flexural or tensile test specimens. A static, dynamic, or cyclic load is applied to each specimen until fracture. Statistical strength and SCG (fatigue) parameters are then determined from these data. Using these parameters and the results obtained from a finite element analysis, the time-dependent reliability for a complex component geometry and loading is then predicted. Appropriate design changes are made until an acceptable probability of failure has been reached.
A bibliography with abstracts was presented in support of AGARD lecture series No. 81. The following areas were covered: (1) program management, (2) design for high reliability, (3) selection of components and parts, (4) environment consideration, (5) reliable packaging, (6) life cycle cost, and (7) case histories.
In 1986, NASA funded a project to modernize the NASA Ames Research Center Unitary Plan Wind Tunnels, including the replacement of obsolescent controls with a modern, automated distributed control system (DCS). The project effort on this system included an independent safety analysis (ISA) of the automation system. The purpose of the ISA was to evaluate the completeness of the hazard analyses which had already been performed on the Modernization Project. The ISA approach followed a tailoring of the risk assessment approach widely used on existing nuclear power plants. The tailoring of the nuclear industry oriented risk assessment approach to the automation system and its role in reliability-based design of the automation system is the subject of this paper.
Theory and techniques for design, analysis, and fault diagnosis of reliable spacecraft data systems
Design analysis and fault diagnosis for spacecraft data systems
A reliability design concept was developed for semistatic structures which combines the prevailing deterministic method with the first-order reliability method. The proposed method surmounts deterministic deficiencies in providing uniformly reliable structures and improved safety audits. It supports risk analyses and reliability selection criterion. The method provides a reliability design factor derived from the reliability criterion which is analogous to the current safety factor for sizing structures and verifying reliability response. The universal first-order reliability method should also be applicable for air and surface vehicles semistatic structures.