Shcool of EEE

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This paper provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved life cycle management of the vast amount of cable assets around the world.

On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs.

This paper presents a case study on a 275 kV oil-filled cable. The condition assessment and diagnosis are based on analysis of cable surface temperature in relation to its current load and insulation dielectric loss. The work was initiated by a local abnormal temperature rise of 5.2 °C in cable surface temperature, which was observed during a routine inspection. The temperature rise occurred at bend area with a length of approximately one metre in the Blue Phase. No PD activity was identified using on-line PD measurement. The relation between cable surface temperature, cable core temperature and cable insulation condition was then simulated based on the thermal model of power cables. According to simulation analysis, poor condition of cable insulation or oil from an oil duct penetrating a region under the cable surface were identified as possible reasons for the problem observed. An in service X-ray scanning technique was employed for further investigation and to aid diagnosis. The X-ray images revealed a slight distortion of the PVC sheath and the presence of multiple voids between cable insulation paper and the lead sheath. It was concluded that an oil leakage from the oil duct to the voids under the cable lead sheath was responsible for the local cable surface temperature rise. The result removed the concern of incipient cable breakdown, and a potential unplanned outage.

Heat removal problem has been a bane of three dimensional integrated circuits (3DICs). Comparing with other passive cooling techniques, microfluidic cooling appears to be an ideal cooling solution due to its high thermal conductivity and scalability. Without regarding to the fact of non-uniform power distribution of integrated circuits, existing microfluidic cooling with uniform cooling effort incurs large thermal gradient and wastes pump power. This can be avoided by the customized non-uniform cooling scheme proposed in this paper. The microfluidic channels are divided into clusters of relatively homogeneous power distribution and an appropriate flow rate setting is applied to each cluster based on the total flow rate and the maximum allowable temperature of the 3DIC. This paper proposes an efficient clustering algorithm to guide the division of microchannels into clusters and the allocation of cooling resources to each cluster in order to achieve an effective microfluidic cooling with minimal total flow rate. A compact steady state thermal simulator has been developed and verified. Supported by this fast and accurate thermal model, the proposed cooling method and clustering algorithm have been applied to a 3D multi-core testbench for simulation. Compared to the uniform flow rate cooling, the maximum temperature and thermal gradient were reduced under the same total flow rate settings. On the other hand, for a specific peak temperature constraint, up to 21.8% saving in total flow rate with moderate thermal gradients is achieved by the proposed clustered microfluidic cooling. been a problem for 2D large scale integrations as technology node scales and clock frequency increases , the stacking of circuits in 3DIC simultaneously increases power density and junction-to-ambient thermal resistances, making the circuits even more vulnerable to thermal induced hazards. Effective and efficient cooling techniques are thus needed to manage the thermal condition of 3D chips.

Existing three-dimensional (3D) integration of multi-core processor-cache system is confronted with the problem of die thermal run-away hazard. This teething problem is addressed by a real-time demand-based thermal management with non-uniform microfluidic cooling in this paper. A novel runtime temperature management is implemented to predict the real-time temperature demand based on software-sensing with prediction-and-correction. A 3D thermal model is developed to cater the real-time microfluidic thermal dynamics. An autoregressive (AR) prediction with correction is implemented by Kalman filtering to predict and correct the runtime power, which is further used to calculate the future temperature demand. With this software-sensing approach, thermal management can be performed in a cyber-physical fashion with real-time sensing, prediction-and-correction and fine-grained control. Compared to the existing works using on-chip temperature sensors, our closed-loop controller with software-sensing avoids the cost of sensor implementation and deployment. Our work analyzes and predicts the fine-grained temperature profile of multi-core processorcache system. It enables a number of microfluidic channels to be adjusted adaptively with different flow-rates to control the system temperature proactively as opposed to the static control with a uniform flow-rate for microfluidic channels. With the proposed cyber-physical temperature management scheme, it is shown that the temperature of multi-core system is suppressed below an acceptable thermal threshold. In fact, the fine-grained flow-rate control also achieves a more even temperature distribution and saves up to 72.1% of total flow-rate compared with uniform flow-rate controls.

This paper presents a cyber-physical (real-time sensepredict-adjust) thermal management for 3D multi-core system by micro-fluidic cooling. Auto Regressive (AR) model is used to predict future workload and the prediction is furnished by Kalman filtering to get rid of noises due to system variation. A thermal model is developed to sense thermal behaviour of the 3D system including micro-fluidic channels and estimate future thermal demand. The use of fine-grained, or non-uniform, flowrate control with channel clustering is then incorporated to adjust flow-rates based on the predicted future thermal demand in a real-time fashion. Experiment results show that under this cyber-physical scheme, the temperature of 3D multi-core cacheprocessor system is well maintained below threshold and a more even temperature distribution is achieved with lower fluid-pump power or total flow-rate overhead. For example, the total flowrate has a significant reduction of 72.1% under the fine-grained flow rate control 1 .

Selection of processing and geometrical parameters is a crucial step in the extrusion process design. Optimized parameters may result in desirable microstructure at minimum load. The purpose of this paper is determination of the optimal cold forward extrusion parameters with the minimization of tool load as the objective function. This paper deals with different optimization approaches in order to determine the optimal values of logarithmic strain, die angle, and friction with the purpose of finding the minimal tool loading obtained by cold forward extrusion process. The obtained extrusion force model as a fitness function was used to carry out the optimization. Based upon the objective function, metaheuristic algorithms such as genetic algorithm and simulated annealing were adopted as optimization methods for finding the optimum values of cold forward extrusion parameters and the obtained results were compared with those in literature. The better results lead to the smallest energy consumption, longer tool life, better formability of the work material, and the quality of the finished product.

In this study a novel optimization method is presented, the so called mine blast algorithm (MBA). The fundamental concepts and ideas of MBA are derived from the explosion of mine bombs in real world. The efficiency of the proposed optimizer is tested via the optimization of several truss structures with discrete variables and its performance is compared with several well-known metaheuristic algorithms. The results show that MBA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers.

One of the main issues facing dental implantation, which makes osseointegration and bone remodeling problematical, is a mismatch of mechanical properties between engineered and native biomaterials. Functionally Graded Materials (FGMs) has been proposed as a potential alternative to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. This research presents some important findings for the FGM dental implant design in optimization of material gradient. Due to the nature of the problem, multi-objective optimization methods should apply for solving multi-criteria problems. The Pareto front was determined using the Multi-Objective Particle Swarm Optimization (MOPSO). The obtained results from the MOPSO confirm the results obtained by the Response Surface Methodology (RSM) in addition to offering further improvements.

Injectable calcium phosphate cements have
been introduced as adjuncts to internal fixation for treating
selected fractures. These cements harden without producing
much heat, develop compressive strength, and are
remodeled slowly in vivo. The main purpose of the cement
is to fill voids in metaphyseal bone, thereby reducing the
need for bone graft. However, such cements may also
improve the holding strength around metal devices in
osteoporotic bone. This paper presents the optimum
mechanical behavior of calcium phosphate cement/hydroxyl
group functionalized multi-walled carbon nanotubes/
bovine serum albumin (CPC/MWCNT-OH/BSA) composites
in terms of compressive strength using well-known
metaheuristic optimizers. The process parameters studied
were wt% of MWCNT-OH (0.2–0.5 wt%) and wt% of
BSA (5–15 wt%). The obtained results from metaheuristic
algorithms were compared with the results from the
response surface methodology (RSM) in the literature. The
results obtained from metaheuristic algorithms outperformed
the results given by the RSM in terms of less error
percentage and high compressive strength.

Water cycle algorithm (WCA) is a new metaheuristic algorithm which the fundamental
concepts of WCA are derived from nature and are based on the observation of water cycle
process and how rivers and streams flow to sea in the real world. In this paper, the task of
sizing optimization of truss structures including discrete and continues variables carried
out using WCA, and the optimization results were compared with other well-known
optimizers. The obtained statistical results show that the WCA is able to provide faster
convergence rate and also manages to achieve better optimal solutions compared to other
efficient optimizers.