Novel carbon materials for thermal energy harvesting

To decrease the consumption of fossil fuels, research has been done on utilizing low grade heat, sourced from industrial waste streams. One promising thermoenergy conversion system is a thermogalvanic cell; it consists of two identical electrodes held at different temperatures that are placed in contact with a redox-based electrolyte [1, 2]. The temperature dependence of the direction of redox reactions allows power to be extracted from the cell [3, 4]. This study aims to increase the power conversion efficiency and reduce the cost of thermogalvanic cells by optimizing the electrolyte and utilizing a carbon based electromaterial, reduced graphene oxide, as electrodes. Thermal conductivity measurements of the K3Fe(CN)6/K4Fe(CN)6 solutions used, indicate that the thermal conductivity decreases from 0.591 to 0.547 W/m K as the concentration is increased from 0.1 to 0.4 M. The lower thermal conductivity allowed a larger temperature gradient to be maintained in the cell. Increasing the electrolyte concentration also resulted in higher power densities, brought about by a decrease in the ohmic overpotential of the cell, which allowed higher values of short circuit current to be generated. The concentration of 0.4 MK3Fe(CN)6/K4Fe(CN)6 is optimal for thermal harvesting applications using R-GO electrodes due to the synergistic effect of the reduction in thermal flux across the cell and the enhancement of power output, on the overall power conversion efficiency. The maximum mass power density obtained using R-GO electrodes was 25.51 W/kg (three orders of magnitude higher than platinum) at a temperature difference of 60 _C and a K3Fe(CN)6/K4Fe(CN)6 concentration of 0.4 M.

Highly sensitive SnO2 nanofiber chemiresistors with a low optimal operating temperature: synergistic effect of Cu2+/Au co-doping

Metal oxide chemiresistors (MOCs) with a low optimal operating temperature, high sensitivity and fast response/recovery are highly promising for various applications, but remain challenging to realize. Herein, we demonstrate that SnO2 nanofibers after being co-doped with Cu2+ and Au show considerably enhanced sensing performances at an unexpectedly decreased operating temperature. A synergistic effect occurs when the two dopants are introduced together. Co-doping may form a novel strategy to the development of ultrasensitive MOCs working at a low optimal temperature. This journal is © the Partner Organisations 2014.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Flight altitude of trans-Sahara migrants in autumn: a comparison of radar observations with predictions from meteorological conditions and water and energy balance models

Radar observations on the altitude of bird migration and altitudinal profiles of meteorological conditions over the Sahara desert are presented for the autumn migratory period. Migratory birds fly at an average altitude of 1016 m (a.s.l.) during the day and 571 m during the night. Weather data served to calculate flight range using two models: an energy model (EM) and an energy-and-water model (EWM). The EM assumes that fuel supply limits flight range whereas the EWM assumes that both fuel and water may limit flight range. Flight ranges estimated with the EM were generally longer than those with the EWM. This indicates that trans-Sahara migrants might have more problems balancing their water than their energy budget. However, if we assume fuel stores to consist of 70% instead of 100% fat (the remainder consisting of 9% protein and 21% water), predicted flight ranges of the EM and EWM largely overlap. Increased oxygen extraction, reduced flight costs, reduced exhaled air temperature, reduced cutaneous water loss and increased tolerance to water loss are potential physiological adaptations that would improve the water budget in migrants. Both the EM and EWM predict optimal flight altitudes in agreement with radar observations in autumn. Optimal flight altitudes are differently predicted by the EM and EWM for nocturnal spring migration. During spring, the EWM predicts moderately higher and the EM substantially higher flight altitudes than during autumn. EWM predictions are therefore in better agreement with radar observations on flight altitude of migrants over the Negev desert in spring than EM predictions.

Departure fuel loads in time-minimizing migrating birds can be explained by the energy costs of being heavy

Lindstrom and Alerstam presented a model that predicts optimal departure fuel loads as a function of the rate of fuel deposition in time-minimizing migrants. The basis of the model is that the coverable distance per unit of fuel deposited, diminishes with increasing fuel load. This is an effect of the increasing flight costs associated with increasing body mass. Lindstrom and Alerstam (1992) found that birds left at lower fuel loads than their model predicted for which they considered various ecological explanations. Alternatively, we hypothesize that the difference between prediction and empirical data might be a result of extra resting metabolic and transport costs associated with an increase in fuel load during stopover. We develop a new version of the Lindstrom and Alerstam (1992) model taking fuel load associated costs during stopover into account. We fit empirical data from rufous hummingbirds Selasphorus rufus and bluethroats Luscinia svecica to this new model. Estimated fuel-load costs are discussed in relation to knowledge presently available on variations in basal metabolic costs and transport costs with body mass. We show that fuel-load costs within a reasonable range can explain the observed departure fuel loads when migrating birds are time minimizers.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Robot coordination for energy-balanced matching and sequence dispatch of robots to events

Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. Here, we present novel algorithms aimed at overcoming the shortcomings of several existing solutions. We propose pairwise distance based matching algorithm (PDM) to eliminate long edges by pairwise exchanges between matching pairs. Our sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energy-balanced variants are obtained. We also present generalizations which handle multiple visits and timing constraints. Our localized algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime.

An efficient MAC protocol with adaptive energy harvesting for machine-to-machine networks

In a machine-to-machine network, the throughput performance plays a very important role. Recently, an attractive energy harvesting technology has shown great potential to the improvement of the network throughput, as it can provide consistent energy for wireless devices to transmit data. Motivated by that, an efficient energy harvesting-based medium access control (MAC) protocol is designed in this paper. In this protocol, different devices first harvest energy adaptively and then contend the transmission opportunities with energy level related priorities. Then, a new model is proposed to obtain the optimal throughput of the network, together with the corresponding hybrid differential evolution algorithm, where the involved variables are energy-harvesting time, contending time, and contending probability. Analytical and simulation results show that the network based on the proposed MAC protocol has greater throughput than that of the traditional methods. In addition, as expected, our scheme has less transmission delay, further enhancing its superiority.

Clustering versus evenly distributing energy dissipation in wireless sensor routing for prolong network lifetime

A novel Cluster Heads (CH) choosing algorithm based on both Minimal Spanning Tree and Maximum Energy resource on sensors, named MSTME, is provided for prolonging lifetime of wireless sensor networks. MSTME can satisfy three principles of optimal CHs: to have the most energy resource among sensors in local clusters, to group approximately the same number of closer sensors into clusters, and to distribute evenly in the networks in terms of location. Simulation shows the network lifetime in MSTME excels its counterparts in two-hop and multi-hop wireless sensor networks.

Energy saving in electric heater of carbon fiber stabilization oven

Carbon fiber is an advanced material with high tensile strength and modulus, ideally suited for light weight applications. Carbon fiber properties are directly dependent on all aspects of production, especially the process step of thermal stabilization. Stabilization is considered to be one of the most critical process steps. Moreover, the stabilization process is the most energy consuming, time consuming and costly step. As oxidation is an exothermic process, constant airflow to uniformly remove heat from all tows across the towband is indispensable. Our approach is to develop an intelligent computational system that can construct an optimal Computational Fluid Dynamics (CFD) solution. In this study, an electrical heater has been designed by CFD modeling and intelligently controlled. The model results show that the uniform airflow and minimum turbulence kinetic energy can be achieved by combining intelligent system technology with CFD analysis strategy.

An energy-aware QoS enhanced method for service computing across clouds and data centers

QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

Multi-criteria evaluation of wave energy projects on the south-east Australian coast

In the last decade, multiple studies focusing on national-scale assessments of the ocean wave energy resource in Australia identified the Southern Margin to be one of the most energetic areas worldwide suitable for the extraction of wave energy for electricity production. While several companies have deployed single unit devices, the next phase of development will most likely be the deployment of parks with dozens of units, introducing the risk of conflicts within the marine space. This paper presents a geo-spatial multi-criteria evaluation approach to identify optimal locations to deploy a wave energy farm while minimizing potential conflicts with other coastal and offshore users. The methodology presented is based around five major criteria: ocean wave climatology, nature of the seabed, distance to key infrastructure, environmental factors and potential conflict with other users such as shipping and fisheries. A case study is presented for an area off the south-east Australian coast using a total of 18 physical, environmental and socio-economic parameters. The spatial restrictions associated with environmental factors, wave climate, as well as conflict of use, resulted in an overall exclusion of 20% of the study area. Highly suitable areas identified ranged between 11 and 34% of the study area based on scenarios with varying criteria weighting. By spatially comparing different scenarios we identified persistence of a highly suitable area of 700 km2 off the coast of Portland across all model domains investigated. We demonstrate the value of incorporation spatial information at the scale relevant to resource exploitation when examining multiple criteria for optimal site selection of Wave Energy Converters over broad geographic regions.