Experimental Research on Heat Transfer Performance in MQL Grinding With Different Nanofluids

An investigation into the effect of nanofluid minimum quantity lubrication (MQL) on the temperatures in surface grinding is presented and discussed. Six types of nanoparticles, namely molybdenum disulfide (MoS2), zirconium dioxide (ZrO2), carbon nanotube (CNT), polycrystalline diamond, aluminum oxide (Al2O3), and silica dioxide (SiO2), are considered to mix individually with a pollution-free palm oil in preparing the nanofluids. A commonly used Ni-based alloy was chosen as the workpiece material. It is shown that CNT nanofluid results in the lowest grinding temperature of 110.7°C and the associated energy proportionality coefficient of 40.1%. The relevant physical properties of the nanofluids such as the coefficient of thermal conductivity, viscosity, surface tension, and the contact state between the droplets and workpiece surface (contact angle) were discussed to shine a light on their effect on the cooling performance. A mathematical model for convective heat transfer coefficient was then developed based on the boundary layer theories.

Experimental Research on Heat Transfer Performance in MQL Grinding With Different Nanofluids

An investigation into the effect of nanofluid minimum quantity lubrication (MQL) on the temperatures in surface grinding is presented and discussed. Six types of nanoparticles, namely molybdenum disulfide (MoS2), zirconium dioxide (ZrO2), carbon nanotube (CNT), polycrystalline diamond, aluminum oxide (Al2O3), and silica dioxide (SiO2), are considered to mix individually with a pollution-free palm oil in preparing the nanofluids. A commonly used Ni-based alloy was chosen as the workpiece material. It is shown that CNT nanofluid results in the lowest grinding temperature of 110.7°C and the associated energy proportionality coefficient of 40.1%. The relevant physical properties of the nanofluids such as the coefficient of thermal conductivity, viscosity, surface tension, and the contact state between the droplets and workpiece surface (contact angle) were discussed to shine a light on their effect on the cooling performance. A mathematical model for convective heat transfer coefficient was then developed based on the boundary layer theories.

Ecope: Task Aware Workload Elastic Scheduling and Customization for Infrastructure

In the past several years, the development in non functional requirement such as CPU and memory has been done. Due to the workload characteristics the energy efficiency of non functional component has made a large coverage. We develop Ecope to attain energy proportionality for different methods of services of virtual machine in data centres’ decrease non functional energy for servers in large data centers. Demonstrate three input methods to illustrate our concept to real world services such as file processing, backend services and content processing. These services are applying on virtual machine in large data centers. In short, our aim is to recognize the preeminent non functional configuration among various workloads.

EEUI: a new measure to monitor and manage energy efficiency in data centers

Purpose Data centers (DCs) are similar to traditional factories in many aspects like response time constraints, limited capacity, and utilization levels. Several indicators have been developed to monitor and compare productivity in manufacturing. However, in DCs most used indicators focus on technical aspects of infrastructure, not efficiency of operations. The purpose of this paper is to rely on operations management to define a commensurate and proportionate DC performance indicator: the energy-efficient utilization indicator (EEUI). EEUI makes objective and comparative assessment of efficiency possible independently of the operating environment and its constraints. Design/methodology/approach The authors followed a design science approach, which follows the practitioner’s initial steps for finding solutions to business relevant problems prior to theory building. Therefore, this approach fits well with this research, as it is primarily motivated by business and management needs. EEUI combines both the amount of energy consumed by different components and their current energy efficiency (EE). It reaches its highest value when all server components are optimally loaded in EE sense. The authors tested EEUI by collecting data from three scientific DCs and performing controlled laboratory tests. Findings The results indicate that the optimization of EEUI makes it possible to run computing resources more efficiently. This leads to a higher EE and throughput of the DC while reducing the carbon footprint associated to DC operations. Both energy-related costs and the total cost of ownership are consequently reduced, since the amount of both energy and hardware resources needed decrease, while improving DC sustainability. Practical implications In comparison with current DC operations, the results imply that using the EEUI could help increase the EE of DCs. In order to optimize the proposed EEUIs, DC managers and operators should use resource management policies that increase the resource usage variation of the jobs being processed in the same computing resources (e.g. servers). Originality/value The paper provides a novel approach to monitor the EE at which computing resources are used. The proposed indicator not only considers the utilization levels at which server components are used but also takes into account their EE and energy proportionality.