1,262 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
Quantifying Robotic Swarm Coverage
In the field of swarm robotics, the design and implementation of spatial
density control laws has received much attention, with less emphasis being
placed on performance evaluation. This work fills that gap by introducing an
error metric that provides a quantitative measure of coverage for use with any
control scheme. The proposed error metric is continuously sensitive to changes
in the swarm distribution, unlike commonly used discretization methods. We
analyze the theoretical and computational properties of the error metric and
propose two benchmarks to which error metric values can be compared. The first
uses the realizable extrema of the error metric to compute the relative error
of an observed swarm distribution. We also show that the error metric extrema
can be used to help choose the swarm size and effective radius of each robot
required to achieve a desired level of coverage. The second benchmark compares
the observed distribution of error metric values to the probability density
function of the error metric when robot positions are randomly sampled from the
target distribution. We demonstrate the utility of this benchmark in assessing
the performance of stochastic control algorithms. We prove that the error
metric obeys a central limit theorem, develop a streamlined method for
performing computations, and place the standard statistical tests used here on
a firm theoretical footing. We provide rigorous theoretical development,
computational methodologies, numerical examples, and MATLAB code for both
benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering
(LNEE). This book contribution is an extension of our ICINCO 2018 conference
paper arXiv:1806.02488. 27 pages, 8 figures, 2 table
Quantitative Assessment of Robotic Swarm Coverage
This paper studies a generally applicable, sensitive, and intuitive error
metric for the assessment of robotic swarm density controller performance.
Inspired by vortex blob numerical methods, it overcomes the shortcomings of a
common strategy based on discretization, and unifies other continuous notions
of coverage. We present two benchmarks against which to compare the error
metric value of a given swarm configuration: non-trivial bounds on the error
metric, and the probability density function of the error metric when robot
positions are sampled at random from the target swarm distribution. We give
rigorous results that this probability density function of the error metric
obeys a central limit theorem, allowing for more efficient numerical
approximation. For both of these benchmarks, we present supporting theory,
computation methodology, examples, and MATLAB implementation code.Comment: Proceedings of the 15th International Conference on Informatics in
Control, Automation and Robotics (ICINCO), Porto, Portugal, 29--31 July 2018.
11 pages, 4 figure
A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures
This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms
Investigations of machining characteristics in upgraded MQL assisted turning of pure titanium alloy using evolutionary algorithms
Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%
A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems
We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive and negative examples of several types of patterns. We
also demonstrate that pattern detection, which is implemented as a model
checking algorithm, performs very well for test data sets different from the
learning sets. We define a quantitative semantics for the logic and integrate
the model checking algorithm with particle swarm optimization in a
computational framework for synthesis of parameters leading to desired patterns
in reaction-diffusion systems
Smart Skin separation control using distributed-input distributed-output, multi-modal actuators, and machine learning
Efficient flow separation control represents significant economic benefit.
This study applies a machine learning algorithm to minimize flow separation in
Smart Skin, a flow control device that features distributed-input and
distributed-output (DIDO). Smart Skin comprises 30 hybrid actuator units, each
integrating a height-adjustable vortex generator and a mini-jet actuator. These
units are deployed on a backward-facing ramp to reduce flow separation in a
distributed manner. To monitor the flow state, distributed pressure taps are
deployed around the multi-modal actuators. Parametric studies indicate that the
mapping between control parameters and separation control performance is
complex. To optimize separation control, a cutting-edge variant of the particle
swarm optimization (PSO-TPME) is used for the control parameters in the Smart
Skin. This algorithm is capable of achieving fast optimization in
high-dimensional parameter spaces. The results demonstrate the efficiency of
PSO-TPME, and the optimized solution significantly outperforms the best result
from the parametric study. These findings represent a promising future of
machine learning-based flow control using distributed actuators and sensors
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