Platelets made of slit and chopped unidirectional, carbon-fiber prepreg are becoming a popular option for use as a high performance molding compound because of their high fiber volume fraction and increased ability to flow compared to continuous fiber systems. As this molding compound is newly introduced to industry, increasing amounts of research have gone into understanding how platelets flow during molding and how components perform mechanically based on the final orientation state of platelets. This work investigates the performance of prepreg platelet molding compound (PPMC) as a viable alternative to continuous fiber systems for use with geometrically complex structural members on vehicles subjected to collisions. In doing so, the crash performance, energy absorption, and failure morphology of crush tubes made with PPMC are investigated and quantified. Then, a simulation methodology is developed to obtain manufacturing-informed performance models to predict the effect of platelet orientation state on mechanical behavior of PPMC components. This methodology uses a building block approach where each block in modeling is verified against closed-form solution (when available) and validated against experimental results. Once confidence is developed in a modeling block, the complexity of the simulation is increased until a component with full platelet orientation distribution is captured. The result is PPMC component models that are capable of predicting mechanical performance in orientation regimes that are not investigated experimentally.

Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-30T19:12:42Z No. of bitstreams: 1 Tese - Kelly A. m. Almeida.pdf: 1155345 bytes, checksum: dec9667081e7741da147db25d66d7fe2 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-30T19:12:55Z (GMT) No. of bitstreams: 1 Tese - Kelly A. m. Almeida.pdf: 1155345 bytes, checksum: dec9667081e7741da147db25d66d7fe2 (MD5) / Made available in DSpace on 2017-08-30T19:12:55Z (GMT). No. of bitstreams: 1 Tese - Kelly A. m. Almeida.pdf: 1155345 bytes, checksum: dec9667081e7741da147db25d66d7fe2 (MD5) Previous issue date: 2017-06-30 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / Let E"(c) be a space of constant sectional curvature c # 0. We prove that minimal or pluriminimal Kahler submanifolds in En(c) x JR are surfaces. For a pluriminimal immersed submanifold into CPn x R, there exists a dense open sub-set that it admits a foliation by holomorphics (or antiholomorphics) submanifolds of CPn . We investigate pluriminimal immersions of compact Kahler manifolds with first Chern class positive into CP" x R. In this case, it is holomorphic (an-tiholoforphic) in the first factor. In addition, for a half isotropic ppmc immersion of Kahler manifolds into hyperbolic space we have that either it is decomposable in Lorentz space, or it comes from ppmc immersion of Rn or it is immersion of surfa-ces with parallel mean curvature. We also prove that ppmc immersion of compact Kahler manifolds with positive first Chern class into hyperbolic space either it is decomposable in Lorentz space, or it comes from ppmc immersion of IR". Keywords: pluriminimal immersion, ppmc immersion, Kahler manifolds, pa-rallel plurimean curvature. / Neste trabalho provamos que variedades Kãhler imersas mínima ou pluriminimante no espaço produto E"(c) x IR, onde En(c) é um espaço de curvatura seccional constante c # O, são superfícies. Enquanto as imersas pluriminimamente em CP" x IR admitem um aberto denso folheado por subvariedades holomorfas ou antiholomorfas de CP". Além disso, para variedades Kãhler compactas com primeira classe de Chern positiva, provamos que as imersões pluríminimas em CP" x IR são holomorfas em CP". Estudamos também imersões ppmc semi-isotrópica de variedades Kãhler no espaço hiperbólico e concluímos que, ou elas são decomponíveis no espaço de Lorentz, ou são provenientes de imersões ppmc no Rn, ou são imersões de superfícies com curvatura média paralela. Como consequência, verificamos que imersões ppmc de variedades Kãhler com primeira classe de Chern positiva no espaço hiperbólico ou são decomponíveis no espaço de Lorentz, ou são provenientes de imersões ppmc no Rn.

abstract: This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit. / Dissertation/Thesis / Ph.D. Educational Psychology 2014