109,266 research outputs found
Keane, H, VX23315
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/396338Surname: KEANE. Given Name(s) or Initials: H. Military Service Number or Last Known Location: VX23315. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 29586.232786
Item: [2016.0049.28631] "Keane, H, VX23315
Letter from Bishop David Keane to Hagan
No full textHolograph letter from Bishop David Keane, The Palace, Corbally, Limerick, to (Hagan); the two dispensations from the H.(oly) O.(ffice) have arrived. There is no urgency for the papal blessing. Giving his approval for Mr. Kirby's ordination. The talk about Armagh has become threadbare
Second-order cone programming formulations for a class of problems in structural optimization
No full textThis paper provides efficient and easy to
implement formulations for two problems in structural
optimization as second-order cone programming
(SOCP) problems based on the minimum compliance
method and derived using the principle of complementary
energy. In truss optimization both single and
multiple loads (where we optimize the worst-case compliance)
are considered. By using a heuristic which is
based on the SOCP duality we can consider a simple
ground structure and add only the members which
improve the compliance of the structure. It is also
shown that thickness optimization is a problem similar
to truss optimization. Examples are given to illustrate
the method developed in this pape
Multidisciplinary design optimization of UAV airframes
No full textIf one considers the problem of converting an aircraft mission profile into an airframe design from an optimization theory perspective, it becomes obvious that the search problem comes with all the trimmings. The design space is large and multidimensional, there are multiple and often highly multimodal objectives and constraints, these depending not only on the design variables, but often on each other as well. Multidisciplinary Design Optimization studies can be conducted at different levels of detail, depending on the chosen trade-off between the size of the design space and the fidelity of the analysis. In this paper we discuss some of the challenges arising at the conceptual level, where simple, but versatile models and low cost analysis tools are used to guide the designer through the first, fundamental decisions of the design process. At the centre of our proposed design workflow lies a parametric geometry, residing in an off-the-shelf Computer-Aided Design (CAD) tool - this provides the models required by the multidisciplinary analyses. We also touch on some of the issues specific to the design of our chosen class of aircraft - Unmanned Air Vehicles (UAVs). To summarize: a CAD-based UAV conceptual design framework is proposed and demonstrated
Multiobjective optimization using surrogates
No full textUntil recently, optimization was regarded as a discipline of rather theoretical interest, with limited real-life applicability due to the comutational or experimental expense involved. Multiobjective optimization was considered as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and parallel computing more companies are buying inexpensive computing clusters that work in parallel. This allows, for example, efficient fusion of surrogates and finite element models into a multiobjective optimization cycle. The research preented here demonstrates this idea using several response surface methods on a pre-selected set of test functions. It shows that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search
Multi-element stochastic reduced basis methods
No full textThis paper presents mutli-element Stochastic Reduced Basis Methods (ME-SRBMs) for solving linear stochastic partial differential equations. In ME-SRBMs, the domain of definition of the random inputs is decomposed into smaller subdomains or random elements. Stochastic Reduced Basis Methods (SRBMs) are employed in each random element to evaluated the response statistics. These elemental statistics are assimilated to compute the overall statistics. The effectiveness of the method is demonstrated by solving the stochastic steady state heat transfer equation on two geometries involving different types of boundary conditions. Numerical studies are conducted to investigate the h-convergence rates of global and local preconditioning strategies
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