Proposed Energy Model Research Articles

Investigation on plastic deformation mechanism of ultra-coarse cemented carbide based on energy analysis

Microstructure evolution and plastic deformation mechanisms of the ultra-coarse cemented carbide compressed at different temperatures were studied based on designed experiments and a proposed energy model. The cemented carbide compressed at high temperature exhibited larger strain, which resulted from the generation and movement of high density of dislocations in WC grain. The energies stored in WC and Co phases and dissipated by heat energy in the deformation were quantified. The larger energy stored in WC grain than that in Co phase indicates significant contribution of the hard matrix to deformation of the ultra-coarse cemented carbide.

Design of Magnetic Induction Based Energy-Efficient WSNs for Nonconventional Media Using Multilayer Transmitter-Enabled Novel Energy Model

Wireless sensor networks (WSNs) are used in nonconventional media such as underground and underwater environment for applications concerning sensing, intrusion detection, monitoring, and exploration. Traditional techniques of communication like electromagnetic are not suitable for nonconventional media, mainly because of high path loss and thus being impractical. In underwater environment, the majority of the research work is based on acoustic communication. However, it faces many challenges like dynamic channel behavior, low data rate, and high propagation delay. Magnetic induction (MI)-based communication is a recently explored technique for nonconventional media applications. This communication method exhibits several promising characteristics such as predictable and constant channel behavior, negligible propagation delay, sufficiently large communication range with high data rate, and stealth underwater operations. In this paper, four possible compensation circuits and multilayer coil structure are suggested for better design of MI transceiver. Herein, the amount of power received by the receiver circuit mainly depends on the structure of the coil used in the transceiver and the type of media through which the signal propagates. Multilayer coil structure is preferred for the proposed design since it performs better than its single-layer counterpart. A novel energy model for MI-based communication link in nonconventional media is proposed. This model describes energy dissipation in the transceiver circuit for MI communication. An analytical model of optimal clustering is also performed with different positions of base station to elaborate the application of the proposed energy model. Performance analysis is also carried out in dry sand, freshwater, and seawater conditions of nonconventional media.

STATISTICAL ANALYSIS AND INTERPRETATION OF THE HYPSOMETRIC UNIFORMITY (NON-UNIFORMITY) FOR THE REALIZATION OF THE GEOSPHERE MODEL OF STABILITY OF GEOSYSTEMS IN DAGHESTAN

Aim. In order to introduce the geosphere sustainability model of geosystems in the territory of the Republic of Dagestan, we conducted a statistical analysis and interpretation using a hypsometric analysis of the territory based on a detailed study of the relief morphology using mathematical statistics and visualization methods.Methods. The morphological structure of the territory is determined using the method of planimetry at given intervals of heights according to a hypsometric map to a scale of 1: 1,000,000; morphometric analysis of the territory was carried out using a greater number of altitudinal gradations at predetermined border intervals above 500 m of height; hypsometric map was visualized using MapInfo Pro GIS.Results and discussion. As a result of the research, it was revealed that the main argument regarding which the energy indicators of the productivity and stability of geosystems are transformed is the relief in its hypsometric characteristics. The numerical values of the variance of the variability confirm the significant hypsometric non-uniformity of the territory. Comparing the obtained results, we conclude that the maximum uniformity (minimum of diversity) is achieved when the entire area belongs to one height gradation, therefore, , and the minimum uniformity (maximum of diversity) corresponds to the uniformity index 1 , that is, when the areas occupied by all gradations of heights are equal, the value is . From this it follows that the hypsometric diversity (non-uniformity) of the territory of Dagestan is close to the maximum value.Conclusion. The findings of the study were used to implement the previously proposed energy model and the stability of geosystems in the preparation of ecological certificate in a number of districts of Dagestan, since the obtained quantitative estimates of the territory hypsometry are necessary as background indicators for the energy projects being developed and stability limits of the geosystems of particular regions and the entire republic.

Including seismic risk mitigation measures into the Levelized Cost Of Electricity in enhanced geothermal systems for optimal siting

The seismic risk associated with deep fluid injection in Enhanced Geothermal Systems can be mitigated by stopping reservoir stimulation when the seismic risk becomes unacceptable or by reducing production flow rates when seismicity occurs during the operational phase. So far, none of these mitigation measures have been included in the Levelized Cost Of Electricity. A meta-model is introduced that estimates the optimal price of electricity, based on an analytical geothermal energy model, and updates this cost to include the outlay for mandatory seismic risk mitigation measures. The proposed energy model computes both electricity production and heat credit. The costs added during reservoir stimulation are based on the probability of abandoning an injection well, based on a traffic-light system, defined as the ratio of scenarios that exceed a given seismic safety threshold in the risk space. In the production phase, the net energy generated is reduced by clipping the production flow rate so that the reservoir's overpressure does not exceed the regional minimum effective stress. Based on a generic geothermal triplet, we investigate the trade-off between heat credit and seismic risk mitigation cost. The added cost, mostly due to financial risk aversion, shifts the optimal site for a plant from between a few kilometres to tens of kilometres away from populated areas, for increasingly vulnerable building stocks. Finally, using a simple yet realistic optimisation strategy, we study the role that a seismic safety standard plays for determining the number of EGS plants that can be sited in a given region.

Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores

This article presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating a model to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency and performance results.

Energy Modeling and Power Measurement for Mobile Robots

To improve the energy efficiency of a mobile robot, a novel energy modeling method for mobile robots is proposed in this paper. The robot can calculate and predict energy consumption through the energy model, which provides a guide to facilitate energy-efficient strategies. The energy consumption of the mobile robot is first modeled by considering three major factors: the sensor system, control system, and motion system. The relationship between the three systems is elaborated by formulas. Then, the model is utilized and experimentally tested in a four-wheeled Mecanum mobile robot. Furthermore, the power measurement methods are discussed. The energy consumption of the sensor system and control system was at the milliwatt level, and a Monsoon power monitor was used to accurately measure the electrical power of the systems. The experimental results showed that the proposed energy model can be used to predict the energy consumption of the robot movement processes in addition to being able to efficiently support the analysis of the energy consumption characteristics of mobile robots.

A smart protocol-level task mapping for energy efficient traffic on network-on-chip

Previous task mapping assumes that applications directly fetch data on remote nodes and build up their energy efficient mapping based on pattern of the NoC traffic. However, the data movement is actually managed by a cache coherence mechanism that executes a much more complicated protocol under application layer to guarantee data correction. Thus, we propose an energy efficient task mapping referring the protocol layer, rather than application layer, to precisely model protocol activities and energy dissipation. By a probabilistic description of cache accessing, we find a efficient method to convert application activities to energy consumption, generating an energy evaluation as a global optimization goal. We also propose a task mapping algorithm to minimize the energy consumption by referring activity intensity of the protocol. The experimental results show that the proposed energy model achieves a precision with less than 2% error and provides a credible quantitative criterion for energy optimization of cache coherence protocols. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.

Kernel scheduling approach for reducing GPU energy consumption

To handle the increasing data scale in broad fields, GPU (Graphic Processing Unit) is integrated with more and more cores to provide powerful computing capability. To obtain high performance, the feature of concurrent kernels is taken by GPU vendors to fully tap the performance of GPU. Although existing GPU supports concurrent kernels to improve the performance, its improvement in performance demands a sustained increasing power. In execution of concurrent kernels, the hardware utilization of GPU resources, the category of kernels and the order of concurrent kernels issued to GPU affect the energy consumption. By keeping eye on above factors, this paper presents a kernel scheduling approach for reducing energy consumption (KSRE) to relieve the energy problem. Firstly, KSRE extracts the features of several kernels to be executed and then classifies them according to their features. Secondly, it obtains potential energy saving effect of concurrent kernels by using the off-line trained regression model. At the end, KSRE schedules kernels based on above information to save energy. To validate the effectiveness of proposed energy model, the experiments are performed and their outcomes show that KSRE is not only effective but also can save energy with performance enhancement as compared to previous works.

Influence of the Ionic Nitriding of Steels in Glow Discharge on the Structure and Properties of the Coatings

The influence of ion nitriding on the physicochemical properties of metal surfaces is studied from the viewpoint of the proposed energy model of gas-discharge technologies with a possibility of combining the parameters of treatment (temperature, pressure, composition of the gas mixture, and the time of saturation) with the energy parameters of the process (current density and voltage).

Dynamic Video Deblurring Using a Locally Adaptive Blur Model.

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental results on publicly available challenging videos demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.

A Theoretical Model for Predicting Fracture Strength and Critical Flaw Size of the ZrB2-ZrC Composites at High Temperatures

This work shows a new rational theoretical model for quantitatively predicting fracture strength and critical flaw size of the ZrB2-ZrC composites at different temperatures, which is based on a new proposed temperature dependent fracture surface energy model and the Griffith criterion. The fracture model takes into account the combined effects of temperature and damage terms (surface flaws and internal flaws) with no any fitting parameters. The predictions of fracture strength and critical flaw size of the ZrB2-ZrC composites at high temperatures agree well with experimental data. Then using the theoretical method, the improvement and design of materials are proposed. The proposed model can be used to predict the fracture strength, find the critical flaw and study the effects of microstructures on the fracture mechanism of the ZrB2-ZrC composites at high temperatures, which thus could become a potential convenient, practical and economical technical means for predicting fracture properties and material design.

Design and Development of COA-Leach: A well-organized Energy Hypothesize Constructed on Energy Price Demonstrating in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of huge collection of small, battery-powered sensor nodes. Advancements in the WSN give a new scope and dimension for implementing WSN in real-time applications. WSN is inexpensively implemented in the desired area to monitor space, things, and the interaction between the things and encompassing space such as surveillance, intelligent alarm, pipeline and location monitoring, natural disaster monitoring, etc. For processing, transmission and aggregation of data for the aforementioned monitoring functions, nodes consume more energy. This becomes a daunting challenge for WSN, since WSNs are limited with the limited power, processing time and storage capacity. In order to overcome resource constraints, authors of this article proposed a new comprehensive energy postulate that depends on the energy cost modeling named Cyclic Optimized Approach–LEACH (COA-LEACH). The model is constructed through thorough examinations of energy spend in a working WSN for deployment. The decision made in the deployment of nodes explicitly affects the sensing quality as well as the energy consumption of the entire network. Authors mainly focused on the self-rearrange for identification of the neighbor, which is useful for the energy conservation. The proposed generalized model is validated by experimented through spotting toxic gas and also authors in sighted the energy requirement for both the existing and proposed energy model.

A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation

Background and ObjectiveState-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery. MethodsThe proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree. ResultsWe evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method. ConclusionsThe proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities.

Pre-feasibility analysis of powering a remote research facility under arid conditions in Kazakhstan

This study evaluates the feasibility of using photovoltaic solar cells and solar water heating in a remote off-grid research facility for scientists in the steppe of Kazakhstan. The objective of the facility is to observe wildlife in this region, especially saiga antelope, whose population has been drastically reduced in recent years. The analysis is conducted using RETScreen software and includes energy, cost, emissions, and financial assessment. The proposed energy model is compared with a traditional base case scenario (based on a diesel boiler and reciprocating engine). Despite the challenges and constraints, the project pays off within its lifespan. It eliminates greenhouse gas emissions and reduces human interference with local wildlife.

An Energy Efficient Code Offloading Approach for Mobile Cloud Computing

Objectives: This research work proposes a methodology for estimating the energy consumption of tasks by considering processor and memory usage. Methods: To facilitate energy efficiency in CPU, the mobile devices may be operated at different frequencies during the execution of tasks. This research work applies CPU frequency scaling as its base to achieve energy efficiency. Besides, it also considers energy consumption during memory access while making the offloading decision. Findings: The proposed approach uses energy consumption during computation as well as memory access as its metric to conceive the offloading decision. Additionally, the proposed energy model is simulated and the results are concluded that there is a considerable amount of energy saving in mobile devices due to computation offloading to nearby mobile devices or cloud resources. Applications: To save energy decide which application in mobile as energy consume considered as model. The models work as code offloading in MATLAB and determine by two level genetic algorithms. The efficiency of proposed model is evaluated by a simulation and average energy result can be concluded for a mobile device.

Building IoT Applications with Raspberry Pi and Low Power IQRF Communication Modules

Typical Internet of Things (IoT) applications involve collecting information automatically from diverse geographically-distributed smart sensors and concentrating the information into more powerful computers. The Raspberry Pi platform has become a very interesting choice for IoT applications for several reasons: (1) good computing power/cost ratio; (2) high availability; it has become a de facto hardware standard; and (3) ease of use; it is based on operating systems with a big community of users. In IoT applications, data are frequently carried by means of wireless sensor networks in which energy consumption is a key issue. Energy consumption is especially relevant for smart sensors that are scattered over wide geographical areas and may need to work unattended on batteries for long intervals of time. In this scenario, it is convenient to ease the construction of IoT applications while keeping energy consumption to a minimum at the sensors. This work proposes a possible gateway implementation with specific technologies. It solves the following research question: how to build gateways for IoT applications with Raspberry Pi and low power IQRF communication modules. The following contributions are presented: (1) one architecture for IoT gateways that integrates data from sensor nodes into a higher level application based on low-cost/low-energy technologies; (2) bindings in Java and C that ease the construction of IoT applications; (3) an empirical model that describes the consumption of the communications at the nodes (smart sensors) and allows scaling their batteries; and (4) validation of the proposed energy model at the battery-operated nodes.

Charge-Transfer State Energy and Its Relationship with Open-Circuit Voltage in an Organic Photovoltaic Device

Charge-transfer state (CTS) plays a very important role in organic photovoltaic (OPV) devices. Especially the relationship between open-circuit voltage (VOC) and CTS has been widely discussed. It is proposed that the CTS energy (ECT) directly determines the VOC value; however, the ECTs measured from different techniques often show considerable discrepancy. Here four methods are applied to probe the ECT values in five different bulk-heterojunction polymer:fullerene OPVs. It is found that linear relationships exist between different ECT’s and VOC values. The detailed energetic meanings of the ECT values measured from different techniques are discussed, and the origin of their discrepancy is analyzed. Lastly, based on a proposed energy model, a relationship is summarized to estimate VOC loss by considering the energetic broadening of CTS, bimolecular recombination, and dielectric effect. The results provide a guideline to forecast the VOC and investigate its loss in OPV.

Power-based electric vehicle energy consumption model: Model development and validation

The limited drive range (The maximum distance that an EV can travel.) of Electric Vehicles (EVs) is one of the major challenges that EV manufacturers are attempting to overcome. To this end, a simple, accurate, and efficient energy consumption model is needed to develop real-time eco-driving and eco-routing systems that can enhance the energy efficiency of EVs and thus extend their travel range. Although numerous publications have focused on the modeling of EV energy consumption levels, these studies are limited to measuring energy consumption of an EV’s control algorithm, macro-project evaluations, or simplified well-to-wheels analyses. Consequently, this paper addresses this need by developing a simple EV energy model that computes an EV’s instantaneous energy consumption using second-by-second vehicle speed, acceleration and roadway grade data as input variables. In doing so, the model estimates the instantaneous braking energy regeneration. The proposed model can be easily implemented in the following applications: in-vehicle, Smartphone eco-driving, eco-routing and transportation simulation software to quantify the network-wide energy consumption levels for a fleet of EVs. One of the main advantages of EVs is their ability to recover energy while braking using a regenerative braking system. State-of-the-art vehicle energy consumption models consider an average constant regenerative braking energy efficiency or regenerative braking factors that are mainly dependent on the vehicle’s average speed. In an attempt to enhance EV energy consumption models, the proposed model computes the regenerative braking efficiency using the instantaneous vehicle operational variables. The proposed model accurately estimates the energy consumption, producing an average error of 5.9% relative to empirical data. The results also demonstrate that EVs can recover a higher amount of energy in an urban driving environment when compared to high speed highway driving using the proposed model. Moreover, the study also compared different electric vehicles and quantified the impact of auxiliary systems, including the air conditioning and heating systems, on vehicle energy consumption levels using the proposed energy model. The study demonstrated that the use of the heating and air conditioning system could significantly reduce the EV efficiency and travel range.

Energy Modelling on Taper Tool Geometry of Aluminium Alloys using C Program

Friction stir welding is solid state joining process where the energy efficiency is dependant on the selection of tool geometry. The objective of this work is to reduce time consumption in calculating tool geometric parameters using C program. As the tool being of different geometry in concern with different complex operation, the energy generated and the temperature distribution which influenced by efficient energy is to be calculated. The calculation is performed based on the machining different work pieces by adjusting tool parameters of aluminium alloys such as Al6061-T6, Al22024-T6, Al7075-T6. Six different cases had been generated in our work for each Aluminium alloy where different RPM (Rotation Per Minute) such as 200, 400, 600, 800, 1000, 1200 was maintained and compared to translational speed and applied normal force. The Energy and Maximum Temperature obtained using Taper Model and the Cylindrical model is compared based on the data processed during the machining. The proposed energy model equation is helpful in computing these data. Manual calculation in these equation may lead to large consumption. Thus the different tool parametric value can be easily generated by coding these equation in C program and feeding necessary input parameters from users. Thereby it generates the different values at faster rate. From this experiment, it is found that abrupt changes in the process may lead influence the efficiency of welding the material and may affect it. Larger data regarding the tool parameter can be processed easily at less time using C. Different tools have different parametric changes and thereby having knowledge over its parameters may not affect the work pieces. C program help to generate these data. Further improvement can be made by doing same study over different other tools and by coding these different values calculated maybe helpful in tool selection. The study can also be made over similar other welding processes too and also help in prevention of damage of different work pieces.

Energy modeling for variable material removal rate machining process: an end face turning case

Material-cutting energy modeling is the key technology of energy modeling of machining process, being the foundation of energy optimization. The material-cutting power changes dynamically during variable-material removal rate (MRR) machining process. Hence, the energy characteristic of variable-MRR machining process is more complicated than that of constant-MRR machining process. In this paper, a modeling method of material-cutting energy for variable-MRR machining process is proposed. The dynamic power characteristics are fully considered in this method, and the impacts of cutting parameters on material-cutting energy are also considered. Experimental studies were conducted to obtain the fitting coefficients of the proposed energy model. Finally, energy calculations of four actual end face turning processes were performed. The results show that the predictive accuracy of all tested end face turning cases is above 90 %. The proposed method provides an accurate energy model for process planning in metal cutting process, which helps manufacturers determinate the energy-optimal process plan.