Investigation of the Mechanism of Temperature Rise in a Data Center With Cold Aisle Containment

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Mingrui Zhang ◽  
Zhengwei Long ◽  
Qingsong An ◽  
Chao Sun ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Temperature Rise ◽  
Data Center ◽  
Pressure Difference ◽  
Static Pressure ◽  
Dynamic Pressure ◽  
Critical Factors ◽  
Factors Affecting ◽  
Variable Space ◽  
Cold Aisle Containment ◽  
Simplified Mathematical Model

This paper theoretically investigates the relationships among factors that affect the temperature rise of server racks and experimentally tests the influence of variable space contained arrangements on the thermal performance. To express the flow and heat transfer process of cold air in servers and analyze the critical factors affecting the temperature rise, a simplified mathematical model representing servers is developed using experimental results. An experiment is conducted within a modular data center in which cold air is supplied from a raised floor. The experiment employed a variable space of cold aisle containment and measured the resulting temperature rise, as well as pressure difference of racks and other parameters, in the simplified mathematical model. By comparing the experimental results and theoretical calculation, the theoretical model is proved to be reasonable and valid. The model predicts that the critical factors affecting the temperature rise of racks consist of static and dynamic pressure difference, total pressure of the fans, geometric structure, power consumption, resistance of doors, and opening area of servers. The result shows that the factor affected by the cold aisle contained system is the static pressure, while for the dynamic pressure difference, the contained architecture has a slight positive effect. Although the average temperature rise is quite decreased in the contained system, the static pressure distribution is nonuniform. A half-contained system which reduced contained space ratio to 50% is measured to cause a 22% increase of the static pressure difference, making a more uniform temperature distribution.

Rotor Wake Generated Unsteady Aerodynamic Response of a Compressor Stator

1978 ◽  
Vol 100 (4) ◽  
pp. 664-675 ◽  
Author(s):  
S. Fleeter ◽  
R. L. Jay ◽  
W. A. Bennett
Keyword(s):  
Pressure Difference ◽  
Second Harmonic ◽  
Incidence Angle ◽  
Dynamic Pressure ◽  
Pressure Differential ◽  
Phase Lag ◽  
Trailing Edge ◽  
Unsteady Pressure ◽  
Forcing Function ◽  
Reduced Frequency

An experimental investigation was conducted to determine the fluctuating pressure distribution on a stationary vane row, with the primary source of excitation being the wakes from the upstream rotor blades. This was accomplished in a large scale, low speed, single stage research compressor. The forcing function, the velocity defect created by the rotor wakes, was measured with a crossed hot-wire probe. The aerodynamic response on the vanes was measured by means of flush mounted high response dynamic pressure transducers. The dynamic data were analyzed to determine the chordwise distribution of the dynamic pressure coefficient and aerodynamic phase lag as referenced to a transverse gust at the vane leading edge. Vane suction and pressure surface data as well as the pressure difference across the vane were obtained for reduced frequency values ranging from 3.65 to 16.80 and for an incidence angle range of 35.5 deg. The pressure difference data were correlated with a state-of-the-art aerodynamic cascade transverse gust analysis. The correlation was quite good for all reduced frequency values for small values of incidence. For the more negative incidence angle data points, it was shown that a convected wake phenomena not modeled in the analysis existed. Both the first and second harmonic unsteady pressure differential magnitude data decrease in the chordwise direction. The second harmonic magnitude data attains a value very nearly zero at the vane trailing edge transducer location, while the first harmonic data is still finite, albeit small, at this location. That the magnitude of the unsteady pressure differential data approaches zero near to the trailing edge, particularly the second harmonic data which has reduced frequency values to 16.8, is significant in that it reflects upon the validity of the Kutta condition for unsteady flows.

scholarly journals Exploring the Critical Factors, the Online Learning Continuance Usage during COVID-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5471
Author(s):  
Chuan-Yu Mo ◽  
Te-Hsin Hsieh ◽  
Chien-Liang Lin ◽  
Yuan Qin Jin ◽  
Yu-Sheng Su
Keyword(s):  
Online Learning ◽  
Technology Acceptance ◽  
Family Support ◽  
Technology Acceptance Model ◽  
Online Teaching ◽  
Ease Of Use ◽  
Critical Factors ◽  
Factors Affecting ◽  
E Learning ◽  
Acceptance Model

In order to enable online learning to continue developing when the COVID-19 pandemic passes, this study aimed to identify the critical factors that affected the use of e-learning by university students during the pandemic. These critical factors will help to increase the efficiency of future development and deployment of online learning systems. Through a literature review, this study employed the technology acceptance model, social support, and task–technology fit as the theoretical basis to establish the framework of the online learning environment with regards to the technology acceptance model in the context of emergency management. A questionnaire survey was administered to students in universities that had implemented online teaching during the pandemic, and 552 valid responses were collected. The survey explored the factors affecting the willingness of higher education institution students to continue using online learning, and the following conclusions were drawn. (1) The easier an online learning platform was to navigate, the better it was perceived by the students, and thus the students were more willing to use it. (2) Ease of use and usefulness were associated with the teachers’ choice of platform and their ability to achieve a satisfactory fit between the course design and platform navigation, which thereby affected the students’ learning outcomes and attitude towards use. (3) The positive attitude of teachers towards teaching increased the students’ perceived ease of use of online learning. (4) During the pandemic, family support—a major support for teachers in online teaching—enhanced teachers’ attitudes towards, and willingness to provide, online teaching. A high level of support showed that the parents urged the students to learn and complete online learning tasks as instructed by the teachers, implying that family support could affect the students’ habits towards, adaptation to, and identification of online learning. The study results provide insights into the factors affecting the willingness of teachers and students to continue using e-learning platforms.

Critical factors affecting the planning and implementation of major projects

1993 ◽  
Vol 14 (5) ◽  
pp. 319-337 ◽  
Author(s):  
John M. Bryson ◽  
Philip Bromiley
Keyword(s):  
Critical Factors ◽  
Factors Affecting

Factors Affecting Productivity of Refuse Compactor Vehicles in India

2021 ◽  
Vol 47 (3) ◽  
pp. 529-535
Author(s):  
Khant Shah ◽  
Devanshu Pandit
Keyword(s):  
Solid Waste Management ◽  
Field Studies ◽  
Operating Costs ◽  
Municipal Solid Waste Management ◽  
Critical Factors ◽  
Factors Affecting ◽  
Operation Optimization ◽  
Selection Parameters ◽  
Evaluation And Monitoring ◽  
Hierarchy Process

Shift towards mechanization in the municipal solid waste management (MSWM) is aimed at improving health wellbeing of the workers who are manually handling waste. With huge investment and operating costs, proper evaluation and monitoring of the performances of these equipment is essential. Identifying and rating the factors which impact the performance of equipment is the first step towards improving efficiency of a system. This research identifies the critical factors and issues in the operation optimization of refuse compactor vehicles. Interviews with experts and field studies helped in identification of 30 factors which were divided in five categories. The research uses analytical hierarchy process (AHP) for finding out the priority vectors of thus identified factors and categories. The study also highlights equipment selection parameters for the best suited model and make.

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