Main Color:
Body Color:
Body Backgrounds:
  • background1
  • background2
  • background3
  • background4
  • background5
  • background6
  • background7
  • background8
  • background9
ASK FOR DEMO

 

 

 

Saturday , December 16 , 2017
White Paper Archive
 

White Papers from Applied Math Modeling Inc.

TN101: Data Center Energy Calculations in CoolSim 4

CoolSim 4 offers the option to perform data center energy calculations. User-specified data as well as results of the CFD calculation are combined to compute a number of standard data center energy metrics, which are reported after the calculation is complete. This document shows how to enter the necessary energy parameters and explains how the energy metrics are calculated. Read more..

WP101: Analysis of the UNH Data Center Using CFD Modeling

In the fall of 2008, Applied Math Modeling Inc, the UNH Data Center, and students from the UNH Mechanical Engineering Department collaborated on a project to improve the design and thermal efficiency of the UNH Data Center, The UNH Data Center is an off-campus facility that consists of approximately 2700 square feet of floor space, divided into three smaller rooms with a common pressurized sub-floor plenum. The goal of the project was first to create a mathematical model of the data center thermal environment using the CoolSim CFD (computational fluid dynamics) application, and then use the model to improve the cooling efficiency of the room. Read more

WP102: Data Center Airflow Modeling helps Facilities Planners Make Informed Decisions

The placement of computer equipment in a new or existing data center is not always obvious. In many cases, common sense placements will give rise to equipment hot spots resulting from inadequate airflow, even though there might be enough total cooling capacity in the room. Computational fluid dynamics (CFD) can be an effective way to simulate the proposed changes and understand their implications prior to implementation. Read more

WP103: Improving Data Center PUE Through Airflow Management

As energy prices continue to rise and concerns about global warming due to carbon emissions continues to grow, there is a growing motive to lower the PUE of data centers worldwide. A careful look at this ratio reveals that the data center cooling system accounts for 75% of the "non-IT" load in the data center Read more

WP104: Improving Model Geometry for CFD Analysis

In today's world, computer aided engineering (CAE) is an integral part of engineering design and analysis. At the root of all CAE is computer-aided-design (CAD), which is used to build virtual models of objects and spaces. Despite their close relationship, CAD models differ from CAE models in certain ways. Read more

WP105: CRAC Thermal Boundary Condition Options in CoolSim

Numerical simulations of data centers require models for the heat generating equipment, such as racks and power density units, and for the computer room air conditioners, or CRACs. This paper reviews the types of boundary conditions that can be placed on CRACs within CoolSim, and under what conditions they should be used.  Read more

WP106: Transient CRAC Failure Analysis

One of the largest concerns facing data center facility manager is the loss of power, particularly in the middle of the night when staff may not be present. The IT equipment is usually protected by uninterruptable power supplies (UPSs), which switch to battery power as soon as the building goes down. The cooling equipment, however, does not have backup power unless generators are installed with automatic starter mechanisms. Predicting the termperature rise on the servers when a cooling system fails is an important step the the overall disaster recovery plan for a data center. Read more

WP107: Using CFD for Data Center Design and Analysis

Computational Fluid Dynamics (CFD) is the numerical simulation of fluid flow. It can be used to predict the fluid velocities, temperatures, and many other variables of interest for a wide variety of application areas.  This paper focuses on how CFD is used to model a data center beginning with an overview of CFD basics, and then describes how data center components can be represented using numerical methods. Read more

WP108: Building Redundancy into a Data Center Cooling System

Large data centers that must offer reliable, on-going service cannot afford interruptions that result from the failure of one or more components. Redundancy is therefore built into the design in many ways. Power to the servers is backed up by banks of batteries (uninterruptable power supplies) that can operate until generators engage following a power outage. Copies of critical applications and data are stored on multiple servers, with one operating and one in reserve at any given time. Read more

WP109: Reducing the Annual Cost of a Telecommunications Data Center

The facilities managers for a large internet service provider have known for a while that one of their data centers is over-cooled. Over-cooling translates into unnecessary energy consumption and expense, so the managers new some changes to the data center were needed. Read more

WP110: Modeling Alternative Cooling Concepts in Coolsim

Traditional data centers are cooled primarily with perimeter CRACs, in-row coolers, and overhead units that are rack-mounted or ceiling mounted. Raised-floor data centers make heavy use of a downflow CRACs while non-raised-floor facilities rely on upflow units, which are sometimes connected to large duct systems to transport the cold air throughout the room. In recent years..Read more

WP111: Modeling Wall-Mounted Coolers in CoolSim 4

Small data centers, and some large ones, occasionally make use of cooling units that are mounted on an outside wall. Residential cooling units, with side-by-side supply and return, are not uncommon in small rooms with fewer than ten racks of equipment. In this paper, CoolSim 4 us used to model a small data center with four wall-mounted coolers. The cooling units are mounted on an outside wall and return air is removed from the data center through either ceiling grilles or grills on the wall. This same method can be used to model air-side economizers and/or roof mounted cooling units. Read more

WP112: Modeling Active Chimneys in CoolSim 4

Rack chimneys, or vertical exhaust ducts, are a popular fixture in data centers. Positioned on top of a cabinet or row of cabinets, they direct the exhaust air upward to minimize mixing with the supply air being drawn to the rack inlets. In data centers with a ceiling return, the chimneys can be connected to the ceiling along with the CRACs to completely segregate the hot and cold air streams. Without a ceiling, they can still improve the performance of the cooling system by stratifying the air, particularly when a downflow CRAC is employed with its return on the top of the unit. Read more

WP113: Modeling Overhead  Duct Systems in CoolSim 4

Non-raised floor data centers are typically outfitted with upflow CRACs.  The supply air, discharged through one or more fans at the top of the CRAC, is delivered to the equipment in the room through the open space at the top of the room, via a ceiling plenum and supply grilles, or through ducts and diffusers .When there is a ceiling plenum or ductwork, the CRAC supplies are connected directly to either the ceiling or the ducts. In this example, CoolSim 4 is used to simulate a data center that is cooled using overhead ducts.  Two methods are illustrated.  For the first, a ceiling plenum is used and the ducts are carved out of the ceiling volume.  For the second, the actual ductwork is created.  Both approaches are easy to setup with CoolSim 4 and allow both simple and complex duct systems to be modeled. Read more

12/30/2015 7:32:40 PM

12/30/2015 7:32:40 PM

1/25/2016 9:47:08 AM

1/25/2016 9:57:18 AM

1/26/2016 8:21:15 AM

1/26/2016 8:54:53 AM

1/26/2016 10:28:51 AM

1/27/2016 7:19:21 AM

1/27/2016 9:02:44 AM

1/28/2016 7:19:09 AM

1/28/2016 7:21:34 AM

1/28/2016 7:26:43 AM

1/29/2016 7:19:15 AM

1/29/2016 7:22:04 AM

1/30/2016 7:19:29 AM

1/30/2016 7:22:49 AM

1/31/2016 7:19:40 AM

1/31/2016 7:23:21 AM

2/1/2016 7:19:54 AM

2/1/2016 7:23:56 AM

2/2/2016 7:20:06 AM

2/2/2016 7:24:19 AM

2/3/2016 7:18:45 AM

2/3/2016 7:23:12 AM

2/4/2016 7:18:49 AM

2/4/2016 7:23:22 AM

2/5/2016 7:19:03 AM

2/5/2016 7:24:07 AM

2/6/2016 7:19:23 AM

2/7/2016 7:19:35 AM

2/7/2016 7:25:12 AM

2/8/2016 4:05:29 AM

2/9/2016 4:05:42 AM

2/10/2016 4:05:55 AM

2/10/2016 4:12:25 AM

2/11/2016 6:25:01 AM

2/11/2016 6:28:06 AM

2/12/2016 6:25:10 AM

2/12/2016 6:28:22 AM

2/12/2016 6:34:49 AM