8,621 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
Multi-Fidelity Methods for Optimization: A Survey
Real-world black-box optimization often involves time-consuming or costly
experiments and simulations. Multi-fidelity optimization (MFO) stands out as a
cost-effective strategy that balances high-fidelity accuracy with computational
efficiency through a hierarchical fidelity approach. This survey presents a
systematic exploration of MFO, underpinned by a novel text mining framework
based on a pre-trained language model. We delve deep into the foundational
principles and methodologies of MFO, focusing on three core components --
multi-fidelity surrogate models, fidelity management strategies, and
optimization techniques. Additionally, this survey highlights the diverse
applications of MFO across several key domains, including machine learning,
engineering design optimization, and scientific discovery, showcasing the
adaptability and effectiveness of MFO in tackling complex computational
challenges. Furthermore, we also envision several emerging challenges and
prospects in the MFO landscape, spanning scalability, the composition of lower
fidelities, and the integration of human-in-the-loop approaches at the
algorithmic level. We also address critical issues related to benchmarking and
the advancement of open science within the MFO community. Overall, this survey
aims to catalyze further research and foster collaborations in MFO, setting the
stage for future innovations and breakthroughs in the field.Comment: 47 pages, 9 figure
A Survey on Surrogate-assisted Efficient Neural Architecture Search
Neural architecture search (NAS) has become increasingly popular in the deep
learning community recently, mainly because it can provide an opportunity to
allow interested users without rich expertise to benefit from the success of
deep neural networks (DNNs). However, NAS is still laborious and time-consuming
because a large number of performance estimations are required during the
search process of NAS, and training DNNs is computationally intensive. To solve
the major limitation of NAS, improving the efficiency of NAS is essential in
the design of NAS. This paper begins with a brief introduction to the general
framework of NAS. Then, the methods for evaluating network candidates under the
proxy metrics are systematically discussed. This is followed by a description
of surrogate-assisted NAS, which is divided into three different categories,
namely Bayesian optimization for NAS, surrogate-assisted evolutionary
algorithms for NAS, and MOP for NAS. Finally, remaining challenges and open
research questions are discussed, and promising research topics are suggested
in this emerging field.Comment: 18 pages, 7 figure
Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design
In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a Δ-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
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