26,954 research outputs found
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Searching for improvement
Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents
Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities
This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuatorsâ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft
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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses âmicroclustersâ and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStreamâs robustness to noise and resiliency to parameter changes
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
In this paper we present a risk-averse model predictive control (MPC) scheme
for the operation of islanded microgrids with very high share of renewable
energy sources. The proposed scheme mitigates the effect of errors in the
determination of the probability distribution of renewable infeed and load.
This allows to use less complex and less accurate forecasting methods and to
formulate low-dimensional scenario-based optimisation problems which are
suitable for control applications. Additionally, the designer may trade
performance for safety by interpolating between the conventional stochastic and
worst-case MPC formulations. The presented risk-averse MPC problem is
formulated as a mixed-integer quadratically-constrained quadratic problem and
its favourable characteristics are demonstrated in a case study. This includes
a sensitivity analysis that illustrates the robustness to load and renewable
power prediction errors
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
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