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Bike positional configuration changes strongly affect cycling performance. While consensus has emerged on saddle height optimisation, there is none for the relationship between other bike positional variables and cycling performance. Accordingly, this systematic review examines the effect of all major positional variables on performance in cycling, assessing differences between cycling disciplines and sex where possible. The systematic review, conducted per PRISMA guidelines, searched databases including Embase, Web of Science, Medline, and CINAHL, screening 16,578 studies. Of these, 47 were fully analysed. Study quality assessment using the NIH tool revealed none rated "good", 5 "fair" and 33 "poor". The analysis involved 724 participants (90 female, 454 male, 180 sex unstated). Studies focused on trunk angle/upper body position, handlebar height, Q factor, foot position, saddle fore-aft/height, seat tube angle and crank length. Participant cycling disciplines were often unspecified and few papers address women cyclists specifically. Key findings were associated with changing saddle height, trunk angle and saddle fore-aft. For trunk angle, accounting for the biomechanical and physiological effects as well as aerodynamic changes is important. Saddle fore-aft affects the hip angle and trunk angle. There are no clear recommendations for crank length, handlebar height, Q factor or cleat position. [ABSTRACT FROM AUTHOR]

Under certain constraints, the optimisation of the position and number of sectional switches in overhead lines in distribution networks can improve the reliability and economy of the power supply. Therefore, a global combination criterion is proposed to simultaneously evaluate the combination performance of multiple switch positions, which avoids the tedious traditional problem of adjusting only one switch position at a time and that can possibly fall into a local-optimum problem. We directly determine the optimal solution and ensure a global optimum. Firstly, the optimal switching position in each line segment is analysed as an alternative switching position. Subsequently, the optimal installation-position combination that corresponds to a given number of switches is determined according to the global combination criterion. If the constraints are satisfied, the number of switches is increased by one, and the optimal installation-position combination is then determined until the constraints are not satisfied. This process ensures not only the global optimum of the switch position but also the optimum number of switches. Finally, examples are given to demonstrate that the proposed method can further optimise the switch configuration. The utility model effectively improves the device utilisation rate, reduces the influence range of the fault, and improves power supply reliability. [ABSTRACT FROM AUTHOR]

Flow control and shape optimisation are fundamental problems in fluid mechanics, particularly in certain scenarios involving ocean engineering. Attempts to manage the flow field via reinforcement learning are based on newly developed deep-learning techniques. By utilising an adaptive optimisation process in the flow around two square cylinders (the main square cylinder with a smaller square cylinder in the front) for the position of the front square cylinder, the flow state that minimises the oscillation of the flow field in the wake can be obtained through deep reinforcement learning. Furthermore, as the training process for this reinforcement learning is time consuming, the flow simulation component of the process is replaced with a feature detection model based on a convolutional neural network, which effectively accelerates the training process. This approach to simulating the optimal position-finding procedure with acceleration can be extended to other similar situations and practical engineering projects. • Flow control using reinforcement learning models to reduce flow-field fluctuation. • Introduction of a flow feature model to rapidly predict the flow amount. • Acceleration using two-way coupling with the flow feature model. [ABSTRACT FROM AUTHOR]

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]

Renewable energy will play a pivotal role in meeting future global energy demand. Of current renewable sources, wave energy offers enormous potential for growth. This research investigates the optimisation of the placement of oscillating buoy-type wave energy converters (WECs). This work explores the design of a wave farm consisting of an array of fully submerged three-tether buoys. In a wave farm, buoy positions strongly determine the farm's output. Optimising the buoy positions is a challenging research problem due to complex and extensive interactions (constructive and destructive) between buoys. This research focuses on maximising the power output of the farm through the placement of buoys in a size-constrained environment, and we propose a new hybrid approach mixing local search, using a surrogate power model, and numerical optimisation methods. The proposed hybrid method is compared with other state-of-the-art search methods in five different wave scenarios – one simplified irregular wave model and four real wave regimes. The new hybrid methods outperform well-known previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes. The best performing method in real-wave scenarios uses the active set non-linear optimisation method to tune final placements. The effectiveness of this method seems to stem for its capacity to search over a larger area than other compared tuning methods. [ABSTRACT FROM AUTHOR]