Reliability-Based Design Optimization of a Composite Airframe Component

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

Housing Collateral, Consumption Insurance and Risk Premia: An Empirical Perpective

In a model with housing collateral, the ratio of housing wealth to human wealth shifts the conditional distribution of asset prices and consumption growth. A decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. Using aggregate data for the US, we find that a decrease in the ratio of housing wealth to human wealth predicts higher returns on stocks. Conditional on this ratio, the covariance of returns with aggregate risk factors explains eighty percent of the cross-sectional variation in annual size and book-to-market portfolio returns. A data appendix for this paper is available .

Sources of the Great Moderation: shocks, frictions, or monetary policy?

We study the sources of the Great Moderation by estimating a variety of medium-scale dynamic stochastic general equilibrium (DSGE) models that incorporate regime switches in shock variances and the inflation target. The best-fit model—the one with two regimes in shock variances—gives quantitatively different dynamics compared with the benchmark constant-parameter model. Our estimates show that three kinds of shocks accounted for most of the Great Moderation and business-cycle fluctuations: capital depreciation shocks, neutral technology shocks, and wage markup shocks. In contrast to the existing literature, we find that changes in the inflation target or shocks in the investment-specific technology played little role in macroeconomic volatility. Moreover, our estimates indicate considerably fewer nominal rigidities than the literature suggests.Econometric models

A QoS-Aware BPEL Framework for Service Selection and Composition Using QoS Properties

Abstract—The promise of service oriented computing, and the availability of web services in particular, promote delivery of services and creation of new services composed of existing services – service components are assembled to achieve integrated computational goals. Business organizations strive to utilize the services and to provide new service solutions and they will need appropriate tools to achieve these goals. As web and internet based services grow into clouds, inter-dependency of services and their complexity increases tremendously. The cloud ontology depicts service layers from a high-level, such as Application and Software, to a low-level, such as Infrastructure and Platform. Each component resides at one layer can be useful to others as a service. It hints the amount of complexity resulting from not only horizontal but also vertical integrations in building and deploying a composite service. Our framework tackles the complexity of the selection and composition issues with additional qualitative information to the service descriptions using Business Process Execution Language (BPEL). Engineers can use BPEL to explore design options, and have the QoS properties analyzed for the design. QoS properties of each service are annotated with our extension to Web Service Description Language (WSDL). In this paper, we describe our framework and illustrate its application to one QoS property, performance. We translate BPEL orchestration and choreography into appropriate queuing networks, and analyze the resulting model to obtain the performance properties of the composed service. Our framework is also designed to support utilizations of other QoS extensions of WSDL, adaptable business logic languages, and composition models for other QoS properties

Model aware execution of composite web services

In the Service Oriented Architecture (SOA) services are computational elements that are published, discovered, consumed and aggregated across platform and organizational borders. The most commonly used technology to achieve SOA are Web Services (WSs). This is due to standardization process (WSDL, SOAP, UDDI standards) and a wide range of available infrastructure and tools. A very interesting aspect of WSs is their composeability. WSs can be easily aggregated into complex workflows, called Composite Web Services (CWSs). These compositions of services enable further reuse and in this way new, even more complex, systems are built.Although there are many languages to specify or implement workflows, in the service-oriented systems BPEL (Business Process Execution Language) is widely accepted. With this language WSs are orchestrated and then executed with specialized engines (like ActiveBPEL). While being very popular, BPEL has certain limitations in monitoring and optimizing executions of CWSs. It is very hard with this language to adapt CWSs to changes in the performance of used WSs, and also to select the optimal way to execute a CWS. To overcome the limitations of BPEL, I present a model-aware approach to execute CWSs. To achieve the model awareness the Coloured Petri Nets (CPN) formalism is considered as the basis of the execution of CWSs. This is different than other works in using formal methods in CWSs, which are restricted to purposes like verification or checking of correctness. Here the formal and unambiguous notation of the CPN is used to model, analyze, execute and monitor CWSs. Furthermore this approach to execute CWSs, which is based on the CPN formalism, is implemented in the model-aware middleware. It is also demonstrated how the middleware improves the performance and reliability of CWSs