Microsoft System Center Configuration Manager Current Branch

2018 ◽  
Author(s):  
Thomas Joos
Keyword(s):  
2009 ◽  
Vol 44 (5) ◽  
pp. 413-418 ◽  
Author(s):  
Kouji Uda ◽  
Ai Kuwasaki ◽  
Kanami Shima ◽  
Tamotsu Matsumoto ◽  
Tomohiko Suzuki

1963 ◽  
Vol 85 (3) ◽  
pp. 237-242
Author(s):  
Arthur D. Brickman ◽  
Barton L. Jenks

Many self-contained machines used in industry serve to generate a sustained mechanical vibration for performing such diverse operations as vibration testing, hammering, material conveying, impacting, and screening. A particular class of such machines having only plane motion is idealized as a dynamic “vibrator” consisting of a two-mass, spring-coupled system driven internally by an oscillatory force. A dynamic analysis of this system is presented to show that the steady-state motion has both translational and rotational components. Specific methods are given for predicting the resultant direction and amplitude of motion for any point in the vibrator system. Results of the dynamic analysis show quantitatively the effect of system resonance, mass distribution, gravity-center configuration, and internal damping on vibrator operation and these design factors are discussed in terms of typical vibrator applications.


Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel

Recent work has proposed an exergy-based strategy to achieve optimal system-wide performance via localized control of individual data center thermal management components. This paper presents the results of a case study where the proposed approach is applied to a data center with two rows of computing racks and two Computer Room Air-Conditioning (CRAC) units. The formulated model is used to predict the optimal data center configuration in terms of supply temperatures, flowrates, and rack heat load configurations. Two extreme cases are chosen: one with the maximum experimental heat load in the data center, and one with a minimal experimental heat load. For each case, the optimal settings for each CRAC unit were predicted using the model and using temperature + flow measurements in the data center. The setpoints predicted by the model for optimal CRAC flow and supply temperature were within 25% of the experimentally determined optima.


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