Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring

The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires the collection of accurate data across various temporal and spatial scales. Accordingly, monitoring systems are seen as central tools for ecosystem-based environmental management, helping on one hand to accurately describe the water column and substrate biophysical properties, and on the other hand to correctly steer sustainability policies by providing timely and useful information to decision-makers. A robust and intelligent sensor network that can adjust and be adapted to different and changing environmental or management demands would revolutionize our capacity to wove accurately model, predict, and manage human impacts on our coastal, marine, and other similar environments. In this paper advanced evolutionary techniques are applied to optimize the design of an innovative energy harvesting device for marine applications. The authors implement an enhanced technique in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization and Genetic Algorithms. Here, this hybrid procedure is applied to a power buoy designed for marine environmental monitoring applications in order to optimize the recovered energy from sea-wave, by selecting the optimal device configuration

OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM

In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy and Boron /Epoxy composite drive shafts using Particle swarm algorithm (PSA). PSA is a population based evolutionary stochastic optimization technique which is a resent heuristic search method, where mechanics are inspired by swarming or collaborative behavior of biological population. PSA programme is developed to optimize the ply stacking sequence with an objective of weight minimization by considering design constraints as torque transmission capacity, fundamental natural frequency, lateral vibration and torsional buckling strength having number of laminates, ply thickness and stacking sequence as design variables. The weight savings of the E-Glass/epoxy and Boron /Epoxy shaft from PAS were 51% and 85 % of the steel shaft respectively. The optimum results of PSA obtained are compared with results of genetic algorithm (GA) results and found that PSA yields better results than GA

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Design optimization of a short-term duty electrical machine for extreme environment

This paper presents design optimisation of a short term duty electrical machine for extreme environments of high temperature and high altitudes. For such extreme environmental conditions of above 80⁰C and altitudes of 30km, thermal loading limits are a critical consideration in machines, especially if high power density and high efficiency are to be achieved. The influence of different material on the performance of such machines is investigated. Also the effect of different slot and pole combinations are studied for machines used for such extreme operating conditions but with short duty. In the research, A Non-dominated Sorting Genetic Algorithm (NSGAII) considering an analytical electromagnetic model, structural and thermal model together with Finite Element (FE) methods are used to optimise the design of the machine for such environments achieving high efficiencies and high power density with relatively minimal computational time. The adopted thermal model is then validated through experiments and then implemented within the Genetic Algorithm (GA). It is shown that, generally, the designs are thermally limited where the pole numbers are limited by volt-amps drawn from the converter. The design consisting of a high slot number allows for improving the current loading and thus, significant weight reduction can be achieved

Two-Stage Method for Optimal Operation of a Distributed Energy System

In this paper, a gas turbine-based distributed energy system (DES) model is developed for the design of operation planning. An operation mode aimed to optimize the operation of this DES is proposed. A multi-objective cost function considering the total system efficiency and operational cost is formulated for the optimal design of DES operation and control. A two-stage approach combining the particle swarm algorithm (PSO) with the sequential quadratic programming (SQP) method is employed to solve the nonlinear programming problem. Optimal operation strategies for the DES are investigated using the proposed two-stage method under three different demand loads in terms of weather conditions. The simulation results are compared with those using traditional rule-based operation methods. It is found that under the proposed operation mode, the DES is capable of achieving an improved performance in terms of thermal efficiency and operational cost

Real time model validation and control of DC motor using matlab and USB

Mechatronics system needs motion or action of some sort. It is created by a force or torque that results in acceleration and displacement. To produce this motion or action, actuators are the device being used. There are many types of actuators and one of the common types of electromechanical actuators is the direct current (DC) motor. The main goal of this project is to estimate the actual model of DC motor and control its speed using an embedded system interfaced to computer. The model identification is achieved using simple and low cost data acquisition system. An Arduino Uno embedded board system is used to collect the data from the sensors, send it to the computer, and control the model. The data processing is performed using MATLAB/SIMULINK. The validation for both the model and the controller are verified through simulations and experiments. The identification and the controllers results were coherent and successful

Lepironia articulata as a sustainable acoustic absorber

Lepironia articulata is found abundant in a swamp and along streams in West Malaysia and it is commonly used for grey water treatment, numerous traditional craft and now commercialise as an organic straw. However, there is a scarcity of knowledge on the physical and acoustical properties of this natural fibre. Therefore, this study was to determine the potential of Lepironia articulata as acoustic absorber. The absorption coefficient was tested using the impedance tube method (ASTM E1050-98) for four different structure arrangements, namely “axial”, “horizontal”, “crossed” and “combination” made up of Lepironia articulata with the diameter ranging from 2 to 4 mm and 4 to 7 mm respectively and the thickness remains at 50 mm. The influence of air gap of 0 to 25 mm, in 5 mm increment was introduced in each sample and other physical properties such as density, porosity and tortuosity were investigated. The results revealed that the Lepironia articulata in horizontal, crossed and combination arrangements showed greatest absorption performance especially in the low frequency range compared to the axial arrangement. If compared between samples with the range diameter of 4 to 7 mm and 2 to 4 mm, bigger stalks diameter in axial arrangement exhibits the least NRC value. Next, air gaps have great influence at low frequency range whereby it shifted the peaks and sound absorption coefficient curve toward lower frequency. Sound absorption coefficient increases as porosity increase and decrease as density�tortuosity increase. Overall, Lepironia articulata has the potential to be used as a sustainable acoustic absorber as all the samples has the NRC value more than or equal to 0.20

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd