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Statistical method checks designs by simulating system operating conditions and adding error factors. Method has potential applicability to commercial and industrial situations where redundancy management system is used to detect and isolate failed components.
This paper addresses the statistical design of CMOS integrated circuits for improved parametric yield. The work uses the Monte Carlo technique of circuit simulation to obtain an unbiased estimation of the yield. A simple graphical analysis tool, the yield factor histogram, is presented. The yield factor histograms are generated by a new computer program called SPICENTER. Using the yield factor histograms, the most sensitive circuit parameters are noted, and their nominal values are changed to improve the yield. Two basic CMOS example circuits, one analog and one digital, are chosen and their designs are 'centered' to illustrate the use of the yield factor histograms for statistical circuit design.
Statistical analysis of atmospheric turbulence at Cape Kennedy Launch Complex for spacecraft design
Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.
The objective of this project was to model the 5-3/4 inch pressure vessels used on the NASA RTOP program in an attempt to learn more about how impact damage forms and what are the residual effects of the resulting damage. A global-local finite element model was developed for the bottle and the states of stress in the bottles were determined down to the constituent level. The experimental data that was generated on the NASA RTOP program was not in a form that enabled the model developed under this grant to be correlated with the experimental data. As a result of this exercise it is recommended that an experimental program be designed using statistical design of experiment techniques to generate data that can be used to isolate the phenomenon that control the formation of impact damage. This data should include residual property determinations so that models for post impact structural integrity can be developed. It is also recommended that the global-local methodology be integrated directly into the finite element code. This will require considerable code development.
This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).
This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in an innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).
The purpose of this module is To provide Institutional Researchers (IRs) with an understanding of the principles of advanced research design and the intermediate/advanced statistical procedures consistent with such designs
It is the purpose of this study to demonstrate the viability and economy of Design-of-Experiments methodologies to arrive at microscale secondary flow control array designs that maintain optimal inlet performance over a wide range of the mission variables and to explore how these statistical methods provide a better understanding of the management of flow in compact air vehicle inlets. These statistical design concepts were used to investigate the robustness properties of low unit strength micro-effector arrays. Low unit strength micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. The term robustness is used in this paper in the same sense as it is used in the industrial problem solving community. It refers to minimizing the effects of the hard-to-control factors that influence the development of a product or process. In Robustness Engineering, the effects of the hard-to-control factors are often called noise , and the hard-to-control factors themselves are referred to as the environmental variables or sometimes as the Taguchi noise variables. Hence Robust Optimization refers to minimizing the effects of the environmental or noise variables on the development (design) of a product or process. In the management of flow in compact inlets, the environmental or noise variables can be identified with the mission variables. Therefore this paper formulates a statistical design methodology that minimizes the impact of variations in the mission variables on inlet performance and demonstrates that these statistical design concepts can lead to simpler inlet flow management systems.
Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.
Statistical analysis of influencial aspects of wind profile disturbances design and dynamic response of space vehicles
The 'cleanroom' software development process is a technical and organizational approach to developing software with certifiable reliability. Key ideas behind the process are well structured software specifications, randomized testing methods and the introduction of statistical controls; but the main point is to deny entry for defects during the development of software. This latter point suggests the use of the term 'cleanroom' in analogy to the defect prevention controls used in the manufacturing of high technology hardware. In the 'cleanroom', the entire software development process is embedded within a formal statistical design, in contrast to executing selected tests and appealing to the randomness of operational settings for drawing statistical inferences. Instead, random testing is introduced as a part of the statistical design itself so that when development and testing are completed, statistical inferences are made about the operation of the system.
Statistical design of experiments (DOE) has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits DOE techniques by accelerating learning, maximizing knowledge, ensuring strategic resource investment, and informing data-driven decisions. In practice, DOE relies on multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to meet challenging research objectives. This presentation shares experiences in the practice of design of experiments at NASA in aeronautics, space exploration, and atmospheric science.
In July of 2011 the Dawn spacecraft is scheduled to begin orbital operations at Vesta, a large main-belt asteroid. Dawn is a NASA Discovery mission that uses solar-electric low-thrust ion propulsion for both interplanetary cruise and orbital operations. Navigating between the Dawn project's four targeted science orbits at Vesta requires a plan that accounts for uncertainties not only in thrust execution, orbit determination, and other spacecraft forces, but also large uncertainties in characteristics of Vesta - such as the asteroid's gravity field and pole orientation. Accommodating these uncertainties requires strategic use of low-thrust maneuvers reserved for statistical trajectory corrections. This paper describes the placement and evaluation of low-thrust statistical maneuvers during two key phases of the Vesta mission along with a discussion of the tools, constraints, and methods used to plan those maneuvers.
The Satellite Servicer System Flight Demonstration (SSSFD) program is a series of Shuttle flights designed to verify major on-orbit satellite servicing capabilities, such as rendezvous and docking of free flyers, Orbital Replacement Unit (ORU) exchange, and fluid transfer. A major part of this system is the manipulator system that will perform the ORU exchange. The manipulator must possess adequate toolplate dexterity to maneuver a variety of EVA-type tools into position to interface with ORU fasteners, connectors, latches, and handles on the satellite, and to move workpieces and ORUs through 6 degree of freedom (dof) space from the Target Vehicle (TV) to the Support Module (SM) and back. Two cost efficient tools were combined to perform a study of robot manipulator design parameters. These tools are graphical computer simulations and Taguchi Design of Experiment methods. Using a graphics platform, an off-the-shelf robot simulation software package, and an experiment designed with Taguchi's approach, the sensitivities of various manipulator kinematic design parameters to performance characteristics are determined with minimal cost.
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The following is a laboratory experiment designed to further understanding of materials science. The experiment itself can be informative for persons of any age past elementary school, and even for some in elementary school. The preparation of the plastic samples is readily accomplished by persons with resonable dexterity in the cutting of paper designs. The completion of the statistical Design of Experiments, which uses Yates' Method, requires basic math (addition and subtraction). Interpretive work requires plotting of data and making observations. Knowledge of statistical methods would be helpful. The purpose of this experiment is to acquaint students with the seven classes of recyclable plastics, and provide hands-on learning about the response of these plastics to mechanical tensile loading.