26,228 research outputs found
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Load-independent characterization of trade-off fronts for operational amplifiers
AbstractâIn emerging design methodologies for analog integrated circuits, the use of performance trade-off fronts, also known as Pareto fronts, is a keystone to overcome the limitations of the traditional top-down methodologies. However, most techniques reported so far to generate the front neglect the effect of the surrounding circuitry (such as the output load impedance) on the Pareto-front, thereby making it only valid for the context where the front was generated. This strongly limits its use in hierarchical analog synthesis because of the heavy dependence of key performances on the surrounding circuitry, but, more importantly, because this circuitry remains unknown until the synthesis process. We will address this problem by proposing a new technique to generate the trade-off fronts that is independent of the load that the circuit has to drive. This idea is exploited for a commonly used circuit, the operational amplifier, and experimental results show that this is a promising approach to solve the issue
Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization
An approach to support the computational aerodynamic design process is presented
and demonstrated through the application of a novel multi-objective variant of
the Tabu Search optimization algorithm for continuous problems to the
aerodynamic design optimization of turbomachinery blades. The aim is to improve
the performance of a specific stage and ultimately of the whole engine. The
integrated system developed for this purpose is described. This combines the
optimizer with an existing geometry parameterization scheme and a well-
established CFD package. The systemâs performance is illustrated through case
studies â one two-dimensional, one three-dimensional â in which flow
characteristics important to the overall performance of turbomachinery blades
are optimized. By showing the designer the trade-off surfaces between the
competing objectives, this approach provides considerable insight into the
design space under consideration and presents the designer with a range of
different Pareto-optimal designs for further consideration. Special emphasis is
given to the dimensionality in objective function space of the optimization
problem, which seeks designs that perform well for a range of flow performance
metrics. The resulting compressor blades achieve their high performance by
exploiting complicated physical mechanisms successfully identified through the
design process. The system can readily be run on parallel computers,
substantially reducing wall-clock run times â a significant benefit when
tackling computationally demanding design problems. Overall optimal performance
is offered by compromise designs on the Pareto trade-off surface revealed
through a true multi-objective design optimization test case. Bearing in mind
the continuing rapid advances in computing power and the benefits discussed,
this approach brings the adoption of such techniques in real-world engineering
design practice a ste
Analytical results for the multi-objective design of model-predictive control
In model-predictive control (MPC), achieving the best closed-loop performance
under a given computational resource is the underlying design consideration.
This paper analyzes the MPC design problem with control performance and
required computational resource as competing design objectives. The proposed
multi-objective design of MPC (MOD-MPC) approach extends current methods that
treat control performance and the computational resource separately -- often
with the latter as a fixed constraint -- which requires the implementation
hardware to be known a priori. The proposed approach focuses on the tuning of
structural MPC parameters, namely sampling time and prediction horizon length,
to produce a set of optimal choices available to the practitioner. The posed
design problem is then analyzed to reveal key properties, including smoothness
of the design objectives and parameter bounds, and establish certain validated
guarantees. Founded on these properties, necessary and sufficient conditions
for an effective and efficient solver are presented, leading to a specialized
multi-objective optimizer for the MOD-MPC being proposed. Finally, two
real-world control problems are used to illustrate the results of the design
approach and importance of the developed conditions for an effective solver of
the MOD-MPC problem
Optimization of Discrete-parameter Multiprocessor Systems using a Novel Ergodic Interpolation Technique
Modern multi-core systems have a large number of design parameters, most of
which are discrete-valued, and this number is likely to keep increasing as chip
complexity rises. Further, the accurate evaluation of a potential design choice
is computationally expensive because it requires detailed cycle-accurate system
simulation. If the discrete parameter space can be embedded into a larger
continuous parameter space, then continuous space techniques can, in principle,
be applied to the system optimization problem. Such continuous space techniques
often scale well with the number of parameters.
We propose a novel technique for embedding the discrete parameter space into
an extended continuous space so that continuous space techniques can be applied
to the embedded problem using cycle accurate simulation for evaluating the
objective function. This embedding is implemented using simulation-based
ergodic interpolation, which, unlike spatial interpolation, produces the
interpolated value within a single simulation run irrespective of the number of
parameters. We have implemented this interpolation scheme in a cycle-based
system simulator. In a characterization study, we observe that the interpolated
performance curves are continuous, piece-wise smooth, and have low statistical
error. We use the ergodic interpolation-based approach to solve a large
multi-core design optimization problem with 31 design parameters. Our results
indicate that continuous space optimization using ergodic interpolation-based
embedding can be a viable approach for large multi-core design optimization
problems.Comment: A short version of this paper will be published in the proceedings of
IEEE MASCOTS 2015 conferenc
Development of an automated aircraft subsystem architecture generation and analysis tool
Purpose â The purpose of this paper is to present a new computational framework to address future
preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate
technologies forming subsystem architectures is enabled with the provision of automated architecture
generation, analysis and optimization. Main focus lies with a demonstration of the frameworks
workings, as well as the optimizers performance with a typical form of application problem.
Design/methodology/approach â The core aspects involve a functional decomposition, coupled
with a synergistic mission performance analysis on the aircraft, architecture and component levels.
This may be followed by a complete enumeration of architectures, combined with a user defined
technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on
ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both
component composition and design parameters. The optimizer is tested on a generic architecture
design problem combined with modified Griewank and parabolic functions for the continuous space.
Findings â Insights from the generalized application problem show consistent rediscovery of the
optimal architectures with the optimizer, as compared to a full problem enumeration. In addition
multi-objective optimization reveals a Pareto front with differences in component composition as well
as continuous parameters.
Research limitations/implications â This paper demonstrates the frameworks application on a
generalized test problem only. Further publication will consider real engineering design problems.
Originality/value â The paper addresses the need for future conceptual design methods of complex
systems to consider a mixed concept space of both discrete and continuous nature via automated methods
- âŠ