Volume 8, Issue 1

In the last decade, piezoelectric energy harvesters have received a significant attention from the scientific community. This comes along with the need of developing self-powered devices such as medical implant to reduce the cost and risk of surgery. This paper investigates a two degree of freedom (2-DOF) piezoelectric energy harvester device to be integrated into a pacemaker. The 2-DOF is designed as a cut-out beam with a secondary beam cut into a primary one. The system is developed to operate in the frequency range of 0–2 Hz, with an acceleration of 1 g (9.8   m/s 2 ) to match the heartbeat frequencies (1–1.67 Hz). The system uses a Lead Zirconate Titanate (PZT) and a Poly Methyl Methacrylate (PMMA) as lead beam to compensate the brittleness of PZT. COMSOL Multiphysics software is used to model and analyze the resonant frequencies of the system, and the stress in the piezoelectric beam. The proposed device has a compact volume of 26 × 11.58 × 0.41 mm, which can fit perfectly in a pacemaker whose battery volume has been reduced by 50%. The output voltage and power are determined through analytical calculus using Matlab. Typical pacemakers require 1 μW to operate. Thus, with a peak power of 30.97 μW at 1.5 Hz and an average output power of 11.05 μW observed from 0.9 to 1.7 Hz, the harvester can power a pacemaker. It is assumed that the energy harvester could extend its life time for 5–10 more years. Furthermore, the harvester operates at extremely low frequency and produces reasonable power, making it suitable for biomedical devices.

A significant amount of work is performed on various aerofoil profiles to improve their characteristics for wind turbine applications. The main purpose is to increase the power output of wind turbines by increasing the lift-to-drag ratio of the aerofoil blade sections. However, most of the developed aerofoil profiles work well only at their design angles of attack and for low Reynolds numbers with a very dramatic stall that could significantly influence the characteristics of the aerofoil profiles and the performance of wind turbines. The present paper is conducted to develop a new aerofoil profile with more gradual stall characteristics that works efficiently for different operational conditions (clean and rough working conditions) similar to those encountered by wind turbines in the free environment. The new aerofoil profile was developed based on a combination between experimental Box–Behnken design and XFOIL code, measurements, and 2D simulation conducted by computational fluid dynamics (CFD) method. The established aerofoil can be used for wind turbine blades because it gives high lift-to-drag-ratios with very smooth and gradual stall characteristics even under very rough operating conditions.

The focus of this paper is to investigate the issue of water jet interference, which is a common flaw in simple reaction turbines. When the turbine’s wall crosses the water jet coming from another nozzle, this is known as jet interference. The governing equations are also used to analyse the Z-Blade simple water reaction turbine for an ideal and practical example, based on the principles of mass-, impulses and energy conservation. Various evaluations of real and potential operating losses for low-head (3–5 m) and low-flow (3 L/s and below) water resources have been conducted. According to experimental data, the Z-Blade turbine Type B achieves the maximum rotational speeds at 450 rpm, followed by Type A at 400 rpm and Type C at 300 rpm. By performing parametric analysis via governing equations, the calculated non-interference speed is approximately twice that of the turbine’s maximum speed. Furthermore, as the turbine reaches its maximum rotational speed at the optimal length diameter, the turbine speed decreases without interference from the jet nozzle rotor. This resembles a phenomenon of non-interference rotor jet on Z-Blade turbine.

Wireless networks involve spatially extended independent sensor nodes, and it is associated with each other’s to preserve and identify physical and environmental conditions of the particular application. The sensor nodes batteries are equipped with restricted energy for working with an energy source. Consequently, efficient energy consumption is themain important challenge in wireless networks, and it is outfitted witharestricted power storage capacity battery. Therefore, routing protocol with energy efficiency is essential in wireless sensor network (WSN) to offer data transmission and connectivity with less energy consumption. As a result, the routing scheme is the main factor for decreasing energy consumption and the network's lifetime. The energy-aware routing model is mainly devised for WSN with high network performance when transmitting data to a sink node. Hence, in this paper, the effectiveness of energy-aware routing protocols in mobile sink-based WSNs is analyzed and justified. Some energy-aware routing systems in mobile sink-based WSN techniques, such as optimizing low-energy adaptive clustering hierarchy (LEACH) clustering approach, hybrid model using fuzzy logic, and mobile sink. The fuzzy TOPSIS-based cluster head selection (CHS) technique, mobile sink-based energy-efficient CHS model, and hybrid Harris Hawk-Salp Swarm (HH-SS) optimization approach are taken for the simulation process. Additionally, the analytical study is executed using various conditions, like simulation, cluster size, nodes, mobile sink speed, and rounds. Moreover, the performance of existing methods is evaluated using various parameters, namely alive node, residual energy, delay, and packet delivery ratio (PDR).

The energy performance of IoT-MWSNs may be augmented by using a suitable clustering technique for integrating IoT sensors. Clustering, on the other hand, requires additional overhead, such as determining the cluster head and cluster formation. Environmental Energy Attentive Clustering with Remote Nodes is a unique environmental energy attentive clustering approach for IoT-MWSNs proposed in this study methodology (E 2 ACRN). Cluster head (CH) in E 2 ACRN is entirely determined by weight. The residual energy of each IoT sensor and the local average energy of all IoT sensors in the cluster are used to calculate the weight. Inappropriately planned allocated clustering techniques might result in nodes being too far away from CH. These distant nodes communicate with the sink by using more energy. The ambient average energy, remoteness among IoT sensors, and sink are used to determine whether a distant node transmits its information to a CH in the previous cycle or to sink in order to lengthen lifetime. The simulation results of the current technique revealed that E 2 ACRN performs better than previous clustering algorithms.