Wind Turbine Noise Reduction through Blade Retrofitting

This paper outlines a plan for the effective reduction of the audible sound level produced by aerodynamic noise from the power-generating turbine blades. The contribution of aerodynamic noise can be divided into two categories: inflow turbulence and airfoil self-noise. The base model and retrofit blade designs were modeled in SolidWorks. Subsequently, noise prediction simulations were conducted and compared to the base blade model to determine which modification provided the greatest benefit using SolidWorks Flow Simulation. The result of this project is a series of blade retrofit recommendations that produce a more acoustically efficient design and reduce noise complaints while enabling turbines to be placed in locations that require quieter operations.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

An adjusted energy-saving quantity calculation method for building energy-efficient retrofit

Aiming at a comprehensive assessment of energy-saving retrofitting effect on existing buildings,a calculation method is developed to adjust energy-saving quantity in standard condition for comparison under the same conditions. A mathematical model,method theory and calculation steps are given. Error analysis results show that this method can be applied accurately to practical engineering projects. In a case study of energy-saving quantity assessment before and after retrofitting on a certain hospital in Shanghai,with energy simulation software TRNSYS,detailed application of this method is introduced and analyzed. The method is applied to the case of energy-saving quantity assessment to a hospital in Shanghai before and after retrofitting with the energy simulation software TRNSYS.

Rolling Decision Model of Thermal Power Retrofit and Generation Expansion Planning Considering Carbon Emissions and Power Balance Risk

With the increasing urgency of the carbon emission reduction task,the generation expansion planning process needs to add carbon emission risk constraints,in addition to considering the level of power adequacy.However,methods for quantifying and assessing carbon emissions and operational risks are lacking.It results in excessive carbon emissions and frequent load-shedding on some days,although meeting annual carbon emission reduction targets.First,in response to the above problems,carbon emission and power balance risk assessment indicators and assessment methods,were proposed to quantify electricity abundance and carbon emission risk level of power planning scenarios,considering power supply regulation and renewable energy fluctuation characteristics.Secondly,building on traditional two-tier models for low-carbon power planning,including investment decisions and operational simulations,considering carbon emissions and power balance risks in lower-tier operational simulations,a two-tier rolling model for thermal power retrofit and generation expansion planning was established.The model includes an investment tier and operation assessment tier and makes year-by-year decisions on the number of thermal power units to be retrofitted and the type and capacity of units to be commissioned.Finally,the rationality and validity of the model were verified through an example analysis,a small-scale power supply system in a certain region is taken as an example.The model can significantly reduce the number of days of carbon emissions risk and ensure that the power balance risk is within the safe limit.

Energy-Efficiency Retrofitting Strategies for Existing Residential Building Envelope System—A Case Study in China

With the rapid development of urbanization, the Chinese government has put equal emphasis on construction and retrofitting. But those projects did not achieve optimal effect because of the lack of targeted and systematic design guidance system. In this study, it first analyzes existing retrofitting methods and sorts into five retrofitting types as a basis, and then, captures the combi-nations and permutations of retrofitting methods and materials by parts and layers to build a database. After that, it combines different kinds of ap-proaches by hierarchical matrix method to conclude the most efficient strat-egy. This study also selects typical residential buildings built between 1980 and 2000 in cold climate area of Northeast China as the research objects to test the integrity and effectiveness. As the results of this paper, it provides systematic guidance and multiple performance-based retrofitting strategies of the existing residential envelope system, which can improve indoor ther-mal comfort with low energy consumption.

Experience on offshore cathodic protection retrofitting in the northern South China Sea

Platforms I and II are steel structures located in offshore areas southeast of the Zhujiang (Pearl) River Delta, the northern South China Sea,. in about 110 in water depth. The jackets, with aluminum sacrificial anodes for cathodic protection (CP) of the immersed zone, were launched in March 1995. In May 096 a CP survey showed that, after almost one year of service, a low polarization level had been achieved and some extended unprotected zones existed; mainly in the deepest part of the Platform II. Further to this, a joint activity was decided in order to assess the need of a possible retrofitting of the CP systems. The results of the activity carried out are dealt with, including technical and economical comparisons amongst several retrofitting options, both with sacrificial anodes and with impressed current systems. The adopted solution is illustrated and data are reported on the level of protection presently achieved.

NOMA-Based Energy-Efficient Task Scheduling in Vehicular Edge Computing Networks: A Self-Imitation Learning-Based Approach

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Seismic retrofitting of reinforced concrete frame structures using GFRP-tube-confined-concrete composite braces

This paper presents a new type of structural bracing intended for seismic retrofitting use in framed structures. This special composite brace, termed glass-fiber-reinforced-polymer （GFRP）-tube-confined-concrete composite brace, is comprised of concrete confined by a GFRP tube and an inner steel core for energy dissipation. Together with a contribution from the GFRP-tube confined concrete, the composite brace shows a substantially increased stiffness to control story drift, which is often a preferred feature in seismic retrofitting. An analysis model is established and implemented in a general finite element analysis program - OpenSees, for simulating the load-displacement behavior of the composite brace. Using this model, a parametric study of the hysteretic behavior （energy dissipation, stiffness, ductility and strength） of the composite brace was conducted under static cyclic loading and it was found that the area ratio of steel core to concrete has the greatest influence among all the parameters considered. To demonstrate the application of the composite brace in seismic retrofitting, a three-story nonductile reinforced concrete （RC） frame structure was retrofitted with the composite braces. Pushover analysis and nonlinear time-history analyses of the retrofitted RC frame structure was performed by employing a suite of 20 strong ground motion earthquake records. The analysis results show that the composite braces can effectively reduce the peak seismic responses of the RC frame structure without significantly increasing the base shear demand.

Resilience-based retrofitting of existing urban RC-frame buildings using seismic isolation

The improvement of the seismic resilience of existing reinforced-concrete(RC) frame buildings, which is essential for the seismic resilience of a city, has become a critical issue. Although seismic isolation is an effective method for improving the resilient performance of such buildings, target-oriented quantitative improvements of the resilient performance of these buildings have been reported rarely. To address this gap, the seismic resilience of two existing RC frame buildings located in a high seismic intensity region of China were assessed based on the Chinese Standard for Seismic Resilience Assessment of Buildings. The critical engineering demand parameters(EDPs) affecting the seismic resilience of such buildings were identified. Subsequently, the seismic resilience of buildings retrofitted with different isolation schemes(i.e., yield ratios) were evaluated and compared, with emphasis on the relationships among yield ratios, EDPs, and levels of seismic resilience. Accordingly, to achieve the highest level of seismic resilience with respect to the Chinese standard, a yield ratio of 3% was recommended and successfully applied to the target-oriented design for the seismic-resilience improvement of an existing RC frame building. The research outcome can provide an important reference for the resilience-based retrofitting of existing RC frame buildings using seismic isolation in urban cities.

Analysis of the impact of large scale seismic retrofitting strategies through the application of a vulnerability-based approach on traditional masonry buildings

The buildings＇ capacity to maintain minimum structural safety levels during natural disasters, such as earthquakes, is recognisably one of the aspects that most influence urban resilience. Moreover, the public investment in risk mitigation strategies is fimdamental, not only to promote social and urban and resilience, but also to limit consequent material, human and environmental losses. Despite the growing awareness of this issue, there is still a vast number of traditional masonry buildings spread throughout many European old city centres that lacks of adequate seismic resistance, requiring therefore urgent retrofitting interventions in order to both reduce their seismic vulnerability and to cope with the increased seismic requirements of recent code standards. Thus, this paper aims at contributing to mitigate the social and economic impacts of earthquake damage scenarios through the development of vulnerability-based comparative analysis of some of the most popularretrofitting techniques applied after the 1998 Azores earthquake. The influence of each technique individually and globally studied resorting to a seismic vulnerability index methodology integrated into a GIS tool and damage and loss scenarios are constructed and critically discussed. Finally, the economic balance resulting from the implementation of that techniques are also examined.

ENERGY-EFFICIENT MICROWAVE COMPONENTS FOR MOBILE COMMUNICATION

In the coexisted world of 3G,4G,5G and many other specialized wireless communication systems,billions of connections could be existing for various information transmission types.Unluckily,data show that the increase of network capacity is heavily more than the increase of the network energy efficiency in recent years,which could lead to more energy consumption per transmitted bit in the future network.As basic units in mobile communication systems,microwave/RF components and modules play key roles

Energy-Efficient UAV Trajectory Design for Backscatter Communication: A Deep Reinforcement Learning Approach

Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.

QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.

Analytical study of performance evaluation for seismic retrofitting of reinforced concrete building using 3D dynamic nonlinear finite element analysis

This paper presents three-dimensional finite element （FE） analyses of an all-frame model of a three-story reinforced concrete （RC） building damaged in the 1999 Taiwan Chi-Chi Earthquake. Non-structural brick walls of the building acted as a seismic resistant element although their contributions were neglected in the design. Hence, the entire structure of a typical frame was modeled and static and dynamic nonlinear analyses were conducted to evaluate the contributions of the brick walls. However, the results of the analyses were considerably overestimated due to coarse mesh discretizations, which were unavoidable due to limited computer resources. This study corrects the overestimations by modifying （1） the tensile strengths and （2） shear stiffness reduction factors of concrete and brick. The results indicate that brick walls improve frame strength although shear failures are caused in columns shortened by spandrel walls. Then, the effectiveness of three types of seismic retrofits is evaluated. The maximum drift of the first floor is reduced by 89.3%, 94.8%, and 27.5% by Steel-confined, FuI1-RC, and Full-brick models, respectively. Finally, feasibility analyses of models with soils were conducted. The analyses indicated that the soils elongate the natural period of building models although no significant differences were observed.

Intervention Retrofitting and Rehabilitation of Al-Gawhara Palace at the Saladin Citadel, Cairo, Egypt

Al-Gawhara Palace also known as Bijou Palace is located in the south of the Mosque of Muhammad Ali in Saladin Citadel in Cairo (Figure 1 and Figure 2). It was commissioned and constructed by Muhammad Ali Pasha in 1814. Al-Gawhara Palace is one of the most famous historical royal palaces in Egypt and the most important modern architectural heritage that reflects an important historical period of the Egyptian modern history—the period of the rule of the family of “Muhammad Ali Pasha”. The causes of structural deficiency of the palace could be attributed to many reasons and actions, mainly due to the earthquakes and seismic events (in particular the Dahshuor earthquake, in October 1992), degradation of drainage and feeding systems leading to water leakage, lack of awareness of the palace values, deterioration resulting from the use of old government agencies and the conflicts among authorities supervising historical buildings. The present study includes many phases: 1) the significance of the original building from the circumstances and date of construction;2) detailed studies of the architectural features and design of the building as well as construction type, methods and techniques of construction;3) characterization of the construction and building materials;4) stability analysis of the structure under static and the impact of seismic loadings;5) engineering measures for intervention retrofitting of the palace (this pilot study presents the main design studies for intervention retrofitting and the rehabilitation and re-employment of Al-Gawhara Palace, started from the static monitoring, old and modern documenting of the current state of preservation thorough the strengthening project implementation;6) the study also presents the reuse designs to convert the palace to be a national museum, for the purpose of preserving it by proposing the best means to apply the correct principles and criteria for reuse and employment in a manner that preserves its value through the functions that fit these deficiencies, and the appropriateness of the present function of the value of minors.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.

Seismic collapse simulation of existing masonry buildings with different retrofitting techniques

Masonry buildings are primarily constructed out of bricks and mortar which become discrete pieces and cannot sustain horizontal forces created by a strong earthquake.The collapse of masonry walls may cause significant human casualties and economic losses.To maintain their integrity,several methods have been developed to retrofit existing masonry buildings,such as the constructional RC frame which has been extensively used in China.In this study,a new method using precast steel reinforced concrete(PSRC)panels is developed.To demonstrate its effectiveness,numerical studies are conducted to investigate and compare the collapse behavior of a structure without retrofitting,retrofitted with a constructional RC frame,and retrofitted with external PSRC walls(PSRCW).Sophisticated finite element models(FEM)were developed and nonlinear time history analyses were carried out.The results show that the existing masonry building is severely damaged under occasional earthquakes,and totally collapsed under rare earthquakes.Both retrofitting techniques improve the seismic performance of existing masonry buildings.However,it is found that several occasional earthquakes caused collapse or partial collapse of the building retrofitted with the constructional RC frame,while the one retrofitted by the proposed PSRC wall system survives even under rare earthquakes.The effectiveness of the proposed retrofitting method on existing masonry buildings is thus fully demonstrated.