1,721,874 research outputs found
Adaptive polynomial chaos for gas turbine compression systems performance analysis
No full textThe design of a gas turbine, or one of its constituent modules, is generally approached with some specific operating
condition in mind (its design point). Unfortunately, engine components seldom exactly meet their specifications and
do not operate at just one condition, but over a range of power settings. This simplification can then lead to a product
that exhibits performance worse than nominal in real-world conditions. The integration of some consideration of
robustness as an active part of the design process can allow products less sensitive to the presence of the noise factors
commonly found in real-world environments to be obtained. To become routinely used as a design tool, minimization
of the time required for robustness analysis is paramount. In this study, a nonintrusive polynomial chaos formulation
is used to evaluate the variability in the performance of a generic modular-core compression system for a three-spool
modern gas turbine engine subject to uncertain operating conditions with a defined probability density function. The
standard orthogonal polynomials from the Askey scheme are replaced by a set of orthonormal polynomials
calculated relative to the specific probability density function, improving the convergence of the method
Accelerating design optimisation via principal components' analysis
No full textIn the last 15 years, automatic design optimisation has been the subject of ever growing
interest, thanks to the development of ever more reliable analysis software, efficient
optimisation methods and powerful computers. Even in industrial environments, design
optimisation is extensively used in a number of disciplines, such as aerodynamics, structures
and many more applications. Better designs in shorter times and clearer trade-offs
between different objectives and disciplines are the most generally recognised advantages.22
The parameterisation represents the “critical enabling factor” for an efficient exploration
of the design space: it is essential to ensure that the parameterisation scheme is able to
cover all feasible designs, in order not to lose potentially good designs, but also that the
minimum possible number of parameters is used, since these affect the size of the design
space and thus the convergence time of the optimiser.
Irrespective of the specific parameterisation technique, the optimisation of complex
products is likely to translate into a large design space and an a priori reduction is not
advisable, as this could reduce the generality of the paramenterisation and lead to the loss
of potentially good designs. Kipouros et al.16 have presented a method for selecting only
the most significant components based on the results of a preliminary optimisation run.
In the present study, a method based on Principal Components’ Analysis is introduced:
given a generic parameterisation, this allows an optimal representation to be derived that
will facilitate a faster and more complete exploration of the design space. The advantages
of this approach are demonstrated through two optimisation test cases from the design of
a core compression system for a three-spool modern turbofan engine
An Integrated System for the Aerodynamic Design of Compression Systems -- Part II: Application
No full textAn Integrated System for the Aerodynamic Design of Compression Systems -- Part I: Development
No full textThe Benefits of Adaptive Parametrization in Multi-objective Tabu Search Optimization
No full textIn real-world optimization problems, large design spaces and conflicting objectives are often combined with
a large number of constraints, resulting in a highly multi-modal, challenging, fragmented landscape. The
local search at the heart of Tabu Search, while being one of its strengths in highly constrained optimization
problems, requires a large number of evaluations per optimization step. In this work, a modification of the
pattern search algorithm is proposed: this modification, based on a Principal Components’Analysis of the
approximation set, allows both a re-alignment of the search directions, thereby creating a more effective
parametrization, and also an informed reduction of the size of the design space itself. These changes make
the optimization process more computationally efficient and more effective – higher quality solutions are
identified in fewer iterations. These advantages are demonstrated on a number of standard analytical test
functions (from the ZDT and DTLZ families) and on a real-world problem (the optimization of an axial
compressor preliminary design)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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