INSDOC’S contribution to bibliometrics

Traces the history of bibliometric research, training and activities in INSDOC. Describes briefly the objectives, facilities, services, research activities, and publications of National Centre on Bibliometrics

INSDOC’S contribution to bibliometrics

Traces the history of bibliometric research, training and activities in INSDOC. Describes briefly the objectives, facilities, services, research activities, and publications of National Centre on Bibliometrics

Comparison of tab-to-busbar ultrasonic joints for electric vehicle li-ion battery applications

Recent uptake in the use of lithium-ion battery packs within electric vehicles has drawn significant attention to the selection of busbar material and corresponding thickness, which are usually based on mechanical, electrical and thermal characteristics of the welded joints, material availability and cost. To determine joint behaviour corresponding to critical-to-quality criteria, this study uses one of the widely used joining technologies, ultrasonic metal welding (UMW), to produce tab-to-busbar joints using copper and aluminium busbars of varying thicknesses. Joints for electrical and thermal characterisation were selected based on the satisfactory mechanical strength determined from the T-peel tests. Electrical contact resistance and corresponding temperature rise at the joints were compared for different tab-to-busbar joints by passing current through the joints. The average resistance or temperature increase from the 0.3 mm Al tab was 0.6 times higher than the 0.3 mm Cu[Ni] tab, irrespective of busbar selection

Adaptive energy minimization of OpenMP parallel applications on many-core systems

Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement