Design for Variation

Reinman, G. L.; Ayer, Timothy; Davan, T.; Devore, Matthew; Finley, Steven; Glanovsky, Jaime; Gray, Lauren; Hall, Benjamin D.; Jones, Charles; Learned, Amanda; Mesaros, Erica; Morris, Robert; Pinero, Sandra; Russo, Randel; Stearns, E. I.

doi:10.1080/08982112.2012.651973

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…Underpinning such computer codes are the variabilities and uncertainties in the input parameters and data, including probabilistic descriptions of engine missions/loads, aerodynamic flow, thermal and stress analysis, combustion effects, and materials performance in harsh environments. At the structures and system level, new concepts for robust design [15] and prognosis [5] are emerging. In addition, recent advancements in Bayesian updating offers a rigorous approach to refine model accuracy based on continuous feedback from diagnostic, maintenance, and operational data [16].…”

Section: A Bright Future For Materials Designmentioning

confidence: 99%

Reducing uncertainty in fatigue life limits of turbine engine alloys

Larsen

Jha

Szczepanski

et al. 2013

International Journal of Fatigue

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a b s t r a c tIn probabilistic design of materials for fracture-critical components in modern military turbine engines, a typical maximum design target risk (DTR) is 5 Â 10 À8 component failures/engine flight hour. This metric underscores the essential role of safety in a design process that simultaneously strives to achieve performance, efficiency, reliability, and affordability throughout the life cycle of the engine. Traditionally, the design and life management approaches for engine materials have typically relied on extensive testing programs to produce large databases of fatigue data, from which statistically based life limits are derived by extrapolation from the mean fatigue behavior. However, we have found that the statistical behavior of fatigue lifetimes under a given test condition often exhibits a bimodal form, and that the trends in mean vs. minimum fatigue lifetime typically respond differently to loading or to microstructural variables. Under such circumstances, the underlying life-limiting mechanisms appear to exhibit a probabilistic microstructural hierarchy in fatigue resistance that is controlled by susceptibility of local microstructural neighborhoods to early damage and the growth of small cracks. These findings suggest that significant opportunities may exist for reductions in uncertainty in materials life-cycle prediction and management, if such hierarchies can be understood and controlled. This paper explores the potential implications of these findings, and a number of possible approaches are suggested for incorporating the insights of life-limiting fatigue into methods of integrated computational materials engineering (ICME) to support optimized life-cycle design of materials and components in turbine engines. Benefits of this approach appear to include substantial improvements in model accuracy, coupled with reduced requirements for materials testing, potentially leading to a significant reduction in the time and cost to develop, validate, transition, and implement new, more fatigue resistant alloys. Published by Elsevier Ltd.

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Section: A Bright Future For Materials Designmentioning

confidence: 99%

Reducing uncertainty in fatigue life limits of turbine engine alloys

Larsen

Jha

Szczepanski

et al. 2013

International Journal of Fatigue

View full text Add to dashboard Cite

show abstract

“…Finally, mechanical engineers seek to create robust systems. This is the motivation behind Design for Variation, a strategic initiative launched at Pratt and Whitney (Reinman et al., ). Central to this initiative is the idea that variation is a major stressor of fielded systems.…”

Section: Why Is Robustness Important?mentioning

confidence: 99%

“…Likewise, the meaning of perturbation is context‐specific. Perturbations include genetic mutations (Wagner, ), the variable temperatures over which the circadian clock or a turbine airfoil must operate (Hatakeyama & Kaneko, ; Reinman et al., ), or changes in load distribution following loss of structural elements (Starossek & Haberland, ). Robustness is important because it allows systems to function across the variable environments they encounter (Gluck et al., ).…”

Section: Why Is Robustness Important?mentioning

confidence: 99%

“…Robustness is also an objective in the design of engineered systems. For example, airplane engines must produce adequate thrust at different altitudes (Reinman, Ayer, Davan, Devore, & Finley, ) and buildings must withstand dynamic forces (Starossek & Haberland, ). The capacity for robust responses to environmental variation is arguably a basic property of all complex, evolved systems (Hammerstein & Stevens, ), whether biological or artifactual.…”

Section: Introductionmentioning

confidence: 99%

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Mechanisms for Robust Cognition

Walsh

Gluck

2014

Cognitive Science

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To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.

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“…Unlike the AMS4928 specification, this MBMD enables prediction of unique location-specific properties. Design for Variation (DFV) methods [14] allow for local minimum property variations (from both simulation predictions and measurements) based on uncertainty of chemistry and processing conditions (Fig. 5).…”

Section: Employing a Model-based Approach Models For Materials And Commentioning

confidence: 99%

Making the Case for a Model-Based Definition of Engineering Materials

Furrer

Dimiduk²,

Cotton

et al. 2017

Integr Mater Manuf Innov

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For over 100 years, designers of aerospace components have used simple requirement-based material and process specifications. The associated standards, product control documents, and testing data provided a certifiable material definition, so as to minimize risk and simplify procurement of materials during the design, manufacture, and operation of engineered systems, such as aerospace platforms. These material definition tools have been assembled to ensure components meet design definitions and design intent. They must ensure the material used meets "equivalency" to that used in the design process. Although remarkably effective, such traditional materials definitions are increasingly becoming the limiting challenge for materials, design, and manufacturing engineers supporting modern, model-based engineering. Demands for cost-effective, higher performance aerospace systems are driving new approaches for multi-disciplinary design optimization methods that are not easily supportable via traditional representations of materials information. Furthermore, property design values having the definitions based on statistical distributions from testing results can leave substantial margin or material capability underutilized, depending on component complexity and the application. Those historical statistical approaches based on macroscopic testing inhibit innovative approaches for enhancing materials definitions for greater performance in design. This can include location-specific properties, hybrid materials, and additively manufactured components. Development and adoption of digital and model-based means of representing engineering materials, within a design environment, is essential to span the widening gap between materials engineering and design. We believe that the traditional approach to defining materials by chemistry ranges, manufacturing process ranges, and static mechanical property minima will migrate to model-based material definitions (MBMDs), due to the many benefits that result from this new capability. This paper reviews aspects of the challenges and opportunities of model-based engineering and model-based definitions.Keywords ICME · MGI · Model-based engineering · Model-based definition · Model-based material definition · Digital engineering · Materials specifications · Representative volume elements · SERVEs

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Design for Variation

Cited by 20 publications

References 12 publications

Reducing uncertainty in fatigue life limits of turbine engine alloys

Reducing uncertainty in fatigue life limits of turbine engine alloys

Mechanisms for Robust Cognition

Making the Case for a Model-Based Definition of Engineering Materials

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