Category Archives: thermal management

Edge Computing and Thermal Management

By Rebecca O’Day and Norman Quesnel
Senior Members of Marketing Staff
Advanced Thermal Solutions, Inc. (ATS)

Expanding the Internet of Things (IOT) into time-critical applications such as with autonomous vehicles, means finding ways to reduce data transfer latency. One such way, edge computing, places some computing as close to connected devices as possible. Edge computing pushes intelligence, processing power and communication capabilities from a network core to the network edge, and from an edge gateway or appliance directly into devices. The benefits include improved response times and better user experiences.

While cloud computing relies on data centers and communication bandwidth to process and analyze data, edge computing provides a means to lay some work off from centralized cloud computing by taking less compute intensive tasks to other components of the architecture, near where data is first collected. Edge computing works with IoT data collected from remote sensors, smartphones, tablets, and machines. This data must be analyzed and reported on in real time to be immediately actionable. [1]

Edge Computing Architecture Scheme with Both the Computing Power and Latency Decreasing Downwards.
FIgure 1: Edge Computing Architecture Scheme with Both the Computing Power and Latency Decreasing Downwards [2]

In the above edge computing scheme, developed by Inovex, the layers are described as follows:

Cloud: On this layer compute power and storage are virtually limitless. But, latencies and the cost of data transport to this layer can be very high. In an edge computing application, the cloud can provide long-term storage and manage the immediate lower levels.

Edge Node: These nodes are located before the last mile of the network, also known as downstream. Edge nodes are devices capable of routing network traffic and usually possess high compute power. The devices range from base stations, routers and switches to small-scale data centers.

Edge Gateway: Edge gateways are like edge nodes but are less powerful. They can speak most common protocols and manage computations that do not require specialized hardware, such as GPUs. Devices on this layer are often used to translate for devices on lower layers. Or, they can provide a platform for lower-level devices such as mobile phones, cars, and various sensing systems, including cameras and motion detectors.

Edge Devices: This layer is home to small devices with very limited resources. Examples include single sensors and embedded systems. These devices are usually purpose-built for a single type of computation and often limited in their communication capabilities. Devices on this layer can include smart watches, traffic lights and environmental sensors. [2]

Today, edge computing is becoming essential where time-to-result must be minimized, such as in smart cars. Bandwidth costs and latency make crunching data near its source more efficient, especially in complex systems like smart and autonomous vehicles that generate terabytes of telemetry data. [3]

Edge Computing and Thermal Management - Leap Mind's Small Edge Computing Device
Figure 2: A Small Scale Edge Computing Device from LeapMind [4]

Besides vehicles, edge computing examples serving the IoT include smart factories and homes, smartphones, tablets, sensor-generated input, robotics, automated machines on manufacturing floors, and distributed analytics servers used for localized computing and analytics.

Major technologies served by edge computing include wireless sensor networks, cooperative distributed peer-to-peer ad-hoc networking and processing, also classifiable as local cloud/fog computing, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality and virtual reality. [5]

Autonomous Vehicles and Smart Cars

New so-called autonomous vehicles have enough computing hardware they could be considered mobile data centers. They generate terabytes of data every day. A single vehicle running for 14 to 16 hours a day creates 1-5TB of raw data an hour and can produce up to 50TB a day. [6]

A moving self-driving car, sending a live stream continuously to servers, could meet disaster while waiting for central cloud servers to process the data and respond back to it. Edge computing allows basic processing, like when to slow down or stop, to be done in the car itself. Edge computing eliminates the dangerous data latency.

Edge Computing Reduces Data Latency to Optimize Systems in Smart and Autonomous Vehicles
Figure 3: Edge Computing Reduces Data Latency to Optimize Systems in Smart and Autonomous Vehicles [7]

Once an autonomous car is parked, nearby edge computing systems can provide added data for future trips. Processing this close to the source reduces the costs and delays associated with uploading to the cloud. Here, the processing does not occur in the vehicle itself.

Other Edge Computing Applications

Edge computing enables industrial and healthcare providers to bring visibility, control, and analytic insights to many parts of an infrastructure and its operations—from factory shop floors to hospital operating rooms, from offshore oil platforms to electricity production.

Machine learning (ML) benefits greatly from edge computing. All the heavy-duty training of ML algorithms can be done on the cloud and the trained model can be deployed on the edge for near real-time or true real-time predictions.

For manufacturing uses, edge computing devices can translate data from proprietary systems to the cloud. The capability of edge technology to perform analytics and optimization locally, provides faster responses for more dynamic applications, such as adjusting line speeds and product accumulation to balance the line. [8]

Figure 4: EdgeBoard by Baidu is a Computing Solution for Edge-Specific Applications [9]

Edge Computing Hardware

Processing power at the edge needs to be matched to the application and the available power to drive an edge system operation. If machine vision, machine learning and other AI technologies are deployed, significant processing power is necessary. If an application is more modest, such as with digital signage, the processing power may be somewhat less.

Intel’s Xeon D-2100 processor is made to support edge computing. It is a lower power, system on chip version of a Xeon cloud/data server processor. The D-2100 has a thermal design point (TDP) of 60-110W.  It can run the same instruction set as traditional Intel server chips, but takes that instruction set to the edge of the network. Typical edge applications for the Xeon D-2100 include multi-access edge computing (MEC), virtual reality/augmented reality, autonomous driving and wireless base stations. [10]

Figure 5: The D-2100 Processor Dissipates Between 60 -110W. Thermal Management Depends on the Type of Device and Where it is Used [11]

Thermal management of the D-2100 edge focused processor is largely determined by the overall mechanical package the edge application takes. For example, if the application is a traditional 1U server, with sufficient air flow into the package, a commercial off the shelf, copper or aluminum heat sink should provide sufficient cooling.  [11]

Edge Computing Server from ATOS Featuring the Intel Xeon D-2187 Edge CPU Processor
Figure 6: An Edge Computing Server from ATOS Featuring the Xeon D-2187 from Intel’s D-2100 Family of Processors [12]

An example of a more traditional package for edge computing is the ATOS system shown in Figure 6. But, for less common packages, where airflow may be less, more elaborate approaches may be needed. For example, heat pipes may be needed to transport excess processor heat to another part of the system for dissipation.

One design uses a vapor chamber integrated with a heat sink. Vapor chambers are effectively flat heat pipes with very high thermal conductance and are especially useful for heat spreading. In edge hardware applications where there is a small hot spot on a processor, a vapor chamber attached to a heat sink can be an effective solution to conduct the heat off the chip.

Coca Cola's Freestyle Fountain An Edge Computing Example
Figure 7: Coca-Cola’s Freestyle Fountain, a Non-Traditional Edge Computing System, Features an Intel I7 CPU, DRAM, Touchscreen, WiFi and HiDef Display [13]

The Nvidia Jetson AGX Xavier is designed for edge computing applications such as logistics robots, factory systems, large industrial UAVs, and other autonomous machines that need high performance processing in an efficient package.

Nvidia Jetson AGX Xavier Edge Computing and AI Processor
Figure 8: Nvidia’s Jetson AGX Xavier Produces Little Heat But Could Have Thermal Issues in Edge Computing Applications [14]

Nvidia has modularized the package, proving the needed supporting semiconductors and input/output ports. While it looks like if could generate a lot of heat, the module only produces 30W and has an embedded thermal transfer plate. However, any edge computing deployment of this module, where it is embedded into an application, can face excess heat issues. A lack of system air, solar loading, impact of heat from nearby devices can negatively impact a module in an edge computing application.

Nvidia Jetson AGX Xavier Processor Development Kit
Figure 9: Nvidia’s Development Kit for the Jetson AGX Xavier Includes Heat Sink and Heat Pipes [15]

Nvidia considers this in their development kit for this module. It has an integrated thermal management solution featuring a heat sink and heat pipes. Heat is transferred from the module’s embedded thermal transfer plate to the heat pipes then to the heat sink that is part of the solution.

For a given edge computing application, a thermal solution might use heat pipes attached to a metal chassis to dissipate heat. Or it could combine a heat sink with an integrated vapor chamber. Studies by Glover, et al from Cisco have noted that for vapor chamber heat sinks, the thermal resistance value varies from 0.19°C/W to 0.23°C/W for 30W of power. [16]

A prominent use case for edge computing is in the smart factory empowered by the Industrial Internet of things (IIoT). As discussed, cloud computing has drawbacks due to latency, reliability through the communication connections, time for data to travel to the cloud, get processed and return. Putting intelligence at the edge can solve many if not all these potential issues. The Texas Instruments (TI) Sitara family of processors was purpose built for these edge computing machine learning applications.

TI Stara ARM Processors for Edge Computing and IIOT
Figure 10: TI’s Sitara Processors are Design for Edge Computing Machine Learning Applications [17]

Smart factories apply machine learning in different ways. One of these is training, where machine learning algorithms use computation methods to learn information directly from a set of data. Another is deployment. Once the algorithm learns, it applies that knowledge to finding patterns or inferring results from other data sets. The results can be better decisions about how a process in a factory is running.  TI’s Sitara family can execute a trained algorithm and make inferences from data sets at the network edge.

The TI Sitara AM57x devices were built to perform machine learning in edge computing applications including industrial robots, computer vision and optical inspection, predictive maintenance (PdM), sound classification and recognition of sound patterns, and tracking, identifying, and counting people and objects. [18,19]

This level of machine learning processing may seem like it would require sophisticated thermal management, but the level of thermal management required is really dictated by the use case. In development of its hardware, TI provides guidance with the implementation of a straight fin heat sink with thermal adhesive tape on its TMDSIDK574 AM574x Industrial Development Kit board.

TI AM574x Industrial Development Kit
Figure 11: TI TMDSIDK574 AM574x Industrial Development Kit [20]

While not likely an economical production product, it provides a solid platform for the development of many of the edge computing applications that are found in smart factories powered by IIoT. The straight fin heat sink with thermal tape is a reasonable recommendation for this kind of application.

Most edge computing applications will not include a lab bench or controlled prototype environment. They might involve hardware for machine vision (an application of computer vision).  An example of a core board that might be used for this kind of application is the Phytec phyCORE-AM57x. [21]

Phytec phyCORE-AM57x for Machine Vision Applications
Figure 12: The Phytec phyCORE-AM57x Can Be used in Edge Computing Machine Vision Applications [22]

Machine vision being used in a harsh, extreme temperature industrial environment might require not just solid thermal management but physical protection as well.  Such a use case could call for thermal management with a chassis. An example is the Arrow SAM Car chassis developed to both cool and protect electronics used for controlling a car.

Chassis for Automotive Application that Protects Components and Provides Thermal Management
Figure 13: Chassis for Automotive Application that Protects Components and Provides Thermal Management [23]

Another packaging example from the SAM Car is the chassis shown below, which is used in a harsh IoT environment. This aluminum enclosure has cut outs and pockets connecting to the chips on the internal PCB.  The chassis acts as the heat sink and provides significant protection in harsh industrial environments.

SAM Car Electronics and Computing Chassis
Figure 14: Aluminum Chassis with Cut Outs and Pocketts to the Enclosed PCB with Semiconductors [23]

Edge computing cabinetry is small in scale (e.g. less than 10 racks), but powerful in information. It can be placed in nearly any environment and location to provide power, efficiency and reliability without the need for the support structure of a larger white space data center. 

The Jetson TX2 Edge Computing Platform from NVIDIA
Figure 15: The Jetson TX2 Edge Computing Platform from Nvidia [24]

Still, racks used in edge cabinets can use high levels of processing power. The enclosure and/or certain components need a built-in, high-performance cooling system.

Hardware OEMs like Rittal build redundancy into edge systems. This lets other IT assets remain fully functional and operational, even if one device fails. Eliminating downtime of the line, preserving key data and rapid response all contribute to a healthier bottom line.

Although edge computing involves fewer racks, the data needs vital cooling protection. For edge computers located in remote locations, the availability of cooling resources may vary. Rittal provides both water and refrigerant-based options. Refrigerant cooling provides flexible installation, water based cooling brings the advantage of ambient air assist, for free cooling. [25]

Immersion Liquid Cooling from LiquidCool
Figure 16: LiquidCool Immersion Cooling Technology Eliminates the Need for Air Cooling

LiquidCool’s technology collects server waste heat inside a fluid system and transports it to an inexpensive remote outside heat exchanger. Or, the waste heat can be re-purposed. In one IT closet-based edge system, fluid-transported waste heat is used for heating an adjacent room. [26]

Green Revolution Cooling provides ICEtank turnkey data centers built inside ISO shipping containers for edge installations nearly anywhere. The ICEtank containers feature immersion cooling systems. Their ElectroSafe coolant protects against corrosion, and the system removes any need for chillers, CRACs (computer room ACs) and other powered cooling systems. [27]

A Summary Chart of Suggested Cooling for Edge Computing

The following chart summarizes air cooling options for Edge Computing applications:

Figure 17: Edge Computing Air Cooling Options Summary Chart
Figure 17: Edge Computing Air Cooling Options Summary Chart [click for larger version]

The Leading Edge

The edge computing marketplace is currently experiencing a period of unprecedented growth. Edge market revenues are predicted to expand to $6.72 billion by 2022 as it supports a global IoT market expected to top $724 billion by 2023. The accumulation of IoT data, and the need to process it at local collection points, will continue to drive the deployment of edge computing. [28,29]

As more businesses and industries shift from enterprise to edge computing, they are bringing the IT network closer to speed up data communications. There are several benefits, including reduced data latency, increased real-time analysis, and resulting efficiencies in operations and data management. Much critical data also stays local, reducing security risks.

References

  1. https://www.networkworld.com/article/3224893/what-is-edge-computing-and-how-it-s-changing-the-network.html
  2. https://www.inovex.de/blog/edge-computing-introduction/ https://www.datacenterknowledge.com/edge-computing/searching-car-network-s-natural-edge
  3. https://www.bloomberg.com/news/articles/2019-06-17/ai-needs-edge-computing-to-make-everyday-devices-smarter
  4. https://www.networkcomputing.com/networking/how-edge-computing-compares-cloud-computing
  5. https://medium.com/velotio-perspectives/a-beginners-guide-to-edge-computing-6cfea853aa11
  6. https://www.datacenterknowledge.com/edge-computing/searching-car-network-s-natural-edge
  7. https://www.wespeakiot.com/will-edge-computing-devour-cloud/
  8. https://www.designnews.com/automation-motion-control/edge-computing-emerges-megatrend-automation/27888481159634
  9. https://www.design-reuse.com/news/45423/xilinx-baidu-brain-edge-ai-edgeboard.html
  10. https://www.intel.com/content/www/us/en/products/docs/processors/xeon/d-2100-brief.html
  11. https://software.intel.com/en-us/articles/intel-xeon-processor-d-2100-product-family-technical-overview
  12. https://atos.net/en/2019/press-release/general-press-releases_2019_05_16/atos-launches-the-worlds-highest-performing-edge-computing-server
  13. https://venturebeat.com/2012/09/11/this-coke-machine-has-an-intel-core-i7-processor-and-it-can-take-your-picture/
  14. https://www.custompcreview.com/news/nvidia-announces-jetson-x2-edge-computing-platform/
  15. https://developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit#resources
  16. “Glover, G., Chen, Y., Luo, A., and Chu, H., “Thin Vapor Chamber Heat Sink and Embedded Heat Pipe Heat Sink Performance Evaluations”, 25th IEEE Symposium, San Jose, CA USA 2009.
  17. http://www.ti.com/tool/SITARA-MACHINE-LEARNING#descriptionArea
  18. https://www.mathworks.com/discovery/machine-learning.html
  19. http://www.ti.com/tool/SITARA-MACHINE-LEARNING#descriptionArea
  20. http://www.ti.com/tool/TMDSIDK574
  21. https://www.phytec.com/phytec-announces-a-new-system-on-module-som-based-on-the-new-sitara-am57x-processor-family-from-texas-instruments/
  22. http://processors.wiki.ti.com/index.php/File:PhyCORE-AM57x_SOM.jpg
  23. https://www.qats.com/cms/2017/10/09/ats-collaborates-sam-car-featured-cnbc-program-jay-lenos-garage/
  24. https://www.custompcreview.com/news/nvidia-announces-jetson-x2-edge-computing-platform/
  25. https://www.rittal.us/contents/edge-computing-and-uncontrolled-environments/
  26. https://www.liquidcoolsolutions.com/edge-server/#single/null
  27. https://www.grcooling.com/edge-computing/
  28. https://blog.apc.com/2019/05/15/four-reasons-configure-to-order-rack-pdus-edge-computing-environments/
  29. https://www.techrepublic.com/article/edge-computing-the-smart-persons-guide/

Industry Developments: Cooling QSFP Optical Transceivers

By Norman Quesnel
Senior Member of Marketing Staff
Advanced Thermal Solutions, Inc.

Rapid advancements in fiber optic technology have increased transfer rates from 10GbE to 40/100GbE within data centers. With the emergence of 100GbE technologies, the creation of data center network architectures free from bandwidth constraints has been made possible. The major enabler of this performance increase is the QSFP optical transceiver.

QSFP is the Quad (4-channel) Small Form-Factor Pluggable optical transceiver standard. A QSFP transceiver interfaces a network device, e.g. switch, router, media converter, to a fiber optic or copper cable connection as part of a Fast Ethernet LAN.

The QSFP design became an industry standard via the Small Form Factor Committee in 2009. Since then, the format has steadily evolved to enable higher data rates. Today, the QSFP MSA (multi-source agreement) specification supports Ethernet, Fibre Channel (FC), InfiniBand and SONET/SDH standards with different data rate options.

QSFP

Fig. 1. The Small QSFP Form Factor Allows More Connectors and Bandwidth than Other Fiber Optic Transceiver Formats. Note the Cooling Fins on Each Receiver Device. [1]

Thermal Issues

The small QSFP form factor has significantly increased the number of ports per package. The increased density of transceivers can lead to heat issues. The optical modules can get hot due to their use of lasers to transmit data. Even though the popular QSFP28 provides lower power dissipation than earlier transceivers – abut 3.5W, the QSFP28 factor has also allowed a significant increase in port density.

Newer microQSFPs can dissipate even more heat. microQSFP interconnects fit more ports (up to 72) on a standard line card, saving significant design space.

Fig 2. Air Gap Locations Shown in Thermal Specifications Feature on QSFP. Top: QSFP at the Inside Edge of a Cage, Bottom: QSFP Section Showing Typical Internal Layout. [2]

The performance and longevity of the transceiver lasers depend on the ambient temperature they operate in and the thermal characteristics of the packaging of these devices. The typical thermal management approach combines heat dissipating fins, e.g. heat sinks, and directed airflow.

Fig 3. Test set-up of different heat sink designs on QSFP28 connector cages. (Advanced Thermal Solutions, Inc.)

Recently, Advanced Thermal Solutions, Inc. (ATS) tested a variety of pin and fin-style heat sinks for their comparative cooling performance on a standard QSFP connector cage. For this setup, an even amount of heat was provided to each connector site via a heater block. Individual thermocouples measured the heat flux resulting with the different heat sink types.

A main goal of this test was how each of four heat sinks would perform while relying on airflow incoming from just one side. By the time it reached the fourth heat sink would the airflow provide enough conduction for adequate cooling? An image from this series of tests is below in Figure 4.

Fig. 4. Test Setup to measure cooling performance of individual heat sinks on a QSFP connector cage when airflow is from one side only. (Advanced Thermal Solutions, Inc.)

The tests results showed that the denser the heat sink pins or fins on the sink closest to the incoming air, the hotter the farthest away QSFP will be. Thus, the best solution used heat sinks whose pin/fin layouts were optimized to work in the actual airflow reaching them.

This meant more open layouts closer to the air source, allowing more air to reach denser pin/fin sinks farther from the air. The non-homogeneous heat sinks allowed for a low, uniform temperature across the QSFP for the most effective function of the QSFPs’ lasers.

microQSFPs

Cooling solutions are different between QSFP28 designs and microQSFP installations. QSFP28 transceiver cooling is typically provided at multiple connector sites. microQSFP modules, e.g. from TE Connectivity, have an integrated heat sink in the individual optical module. Used with connection cages that are optimized for airflow, their heat is controlled in high density applications.

Fig. 5. Integrated Module Thermal Solution (Fins) on microQSFPs Provides Better Thermal Performance and Uses Less Energy for Air Cooling. [3]

Fig. 6. A Video Demo from TE Connectivity Shows 72 Ports of microQSFP Transceivers Units Running at 5W Each and All Kept Under 55°C Temperature Using 82 CFM Airflow. [4]

Finally, another factor affecting cooling performance is surface finish and flatness. Designers can reduce thermal spreading losses by keeping the heat sources close to the thermal interface area and by increasing the thermal conductivity of the case materials.

For QSFP, the size of the cage hole for heat sink contact given in the multi-source agreement (MSA) can be increased giving a reduction in the thermal interface resistance and therefore module temperature.

References:
1. FMAD IO, http://fmad.io/images/blog/20160612-100g-connectors.png
2. https://arkansashq.wordpress.com/2016/10/11/pluggable-optics-modules-thermal-specifications-part-2/
3. microQSFP, http://www.microqsfp.com/
4. TE Connectivity, https://www.youtube.com/watch?v=k_qNj-yAKz4

For more information about Advanced Thermal Solutions, Inc. (ATS) thermal management consulting and design services, visit www.qats.com or contact ATS at 781.769.2800 or ats-hq@qats.com.

Follow ATS on Twitter and Instagram for #ThermalEngineerDay

National Thermal Engineer Day is finally here! Make sure to follow Advanced Thermal Solutions, Inc. (ATS) on Twitter (@qats) and on Instagram (@advancedthermsolutions) to see photos and watch videos from the ATS celebration.

Join in the social media conversation about this national day of recognition by using the hashtag #ThermalEngineerDay. Make sure to share your photos and the ways that you have taken time to celebrate the accomplishments and contributions of thermal engineers.

For more information about National Thermal Engineer Day, click https://www.qats.com/cms/national-thermal-engineer-day.

Analysis of Fan Curves and Fan Laws in Thermal Management of Electronics

This is the second installment in a two-part series examining the use of fans in the thermal management of electronics. Part one, which can be found at https://www.qats.com/cms/2017/03/06/utilizing-fans-thermal-management-electronics-systems, took a closer look at the common types of fans and blowers and the factors that engineers should consider when picking a fan.

In part two, basic fan laws will be explored, as well as using fan curves to analyze fan performance in a system. These standard calculations can help engineers establish boundary conditions for air velocity and pressure drop and ensure that these will meet the thermal requirements (e.g. ambient and junction temperature) of the system.

Fan Laws

CFD simulations of air velocity in a system with fans drawing air across high-powered components. Utilizing fan curves and fan laws enabled ATS engineers to establish the parameters for a successful use of fans for cooling this system. (Advanced Thermal Solutions, Inc.)

FAN LAWS

As noted by Mike Turner of Comair Rotron in “All You Need to Know About Fans,” the primary principle for determining whether or not a fan work within a particular system is that “any given fan can only deliver one flow at one pressure in a particular system.” Each fan has a specific operating point that can be discovered on the fan curve at the intersection of fan static pressure curve and the system pressure curve. Turner advises, “It is best to select a fan that will give an operating point being toward the high flow, low pressure end of the performance curve to maintain propeller efficiency and to avoid propeller stall.”

Before getting to the fan curve though, engineers must run through basic calculations to understand the conditions of the systems in which the fans will be placed. The three basic fan laws, according to Eldridge USA, are as follows:

Fan Laws

While those fan laws will tell you about the specific fans, it is also critical to examine the system in which the fans will be operating. Among the equations that can be used to characterize a system are Volumetric Flow Rate, Mass Flow Rate, Pressure, Power, and Sound (equations are listed below).

Fan Laws

A Qpedia Thermal eMagazine article entitled, “How to Use Fan Curves and Laws in Thermal Design,” added:

“Published fan laws apply to applications where a fan’s air flow rate and pressure are independent of the Reynolds number. In some applications, however, fan performance is not independent and thus the change in Reynolds number should be incorporated into the equation. To determine if the Reynolds number needs to be considered, it must first be calculated.

“According to AMCA specifications, an axial fan’s minimum Reynolds number is 2.0×106. When the calculated Reynolds number is above this value, its effects can be ignored.”

The equation to calculate the Reynolds number is as follows:

Fan Law

In an “Engineering Letter” from The New York Blower Company, it was explained that fan laws only work “within a fixed system with no change in the aerodynamics or airflow characteristics of the system.” In the case of electronics cooling, in which the system requirements will be mostly consistent (with margins for error in case of max power usage), these laws will govern the capabilities of the fans to provide the necessary forced convection cooling for the components in the system.

The Engineering Letter continued, “During the process of system design, the fan laws can be helpful in determining the alternate performance criteria or in developing a maximum/minimum range.” A Qpedia article entitled, “Designing Efficient Fans for Electronics Cooling Applications,” added, “As a general rule, fan efficiency increases with blade diameter and rotational speed.”

There are tools that can assist engineers in the calculation of these basic fan laws, including fan calculators, such as the one provided by Twin City Fans & Blowers.

ANALYZING FAN CURVES AND FAN PERFORMANCE

The aerodynamics of a fan can be charted in a fan curve, which displays the static pressure of the system dependent on the amount of air flow. As Turner noted, fan curves are read from right to left, beginning “with healthy aerodynamic flow and follow it through to aerodynamic stall.” Turner continued, “It is best to select a fan that will give an operating point being toward the high flow, low pressure end of the performance curve to maintain propeller efficiency and to avoid propeller stall.”

Fan Laws

An example of a basic fan curve with static pressure on the Y-axis and airflow on the X-axis. Fan curves are read from right to left beginning with healthy airflow.

There are means for testing fan curves, such as the FCM-100 Fan Characterization Module (pictured below) from Advanced Thermal Solutions, Inc. (ATS). The FCM-100 is specially designed with flow restriction plates that allow the user to control pressure drop across the system during testing. Used in conjunction with pressure and velocity measurement equipment, it verifies manufacturer performance data.

Fan Laws

The ATS FCM-100 Fan Characterization Module is a specialized unit designed to test and characterize fans of various sizes and performance outputs. (Advanced Thermal Solutions, Inc.)

The Qpedia article on fan curves explained, “During a typical fan test, a dozen or more operating points are plotted for pressure and flow rate, and from this data a fan curve is constructed.”

Once a fan curve is determined, it is possible to examine the data and find the operating range for the fans that will meet the thermal requirements of a system. It is also important to note a section in the fan curve, often referred to as the knee of the curve in which the relationship between flow rate and static pressure is no longer easy to predict. There is no longer an easily recognizable, calculable relationship between how a change in one will affect the other.

ATS field application engineer Vineet Barot explained how he analyzed fan curve data, particularly the knee of the curve, in a recent project:

“This is flow rate versus pressure. The more pressure you have in front of a fan, the slower it can pump out the air and this is the curve that determines that.

Fan Laws

Fan operating points on the board, determined by CFD simulations. (Advanced Thermal Solutions, Inc.)

“This little area here is sometime called the knee of the fan curve. Let’s say we’re in this area, the flow rate and pressure is relatively linear, so if I increase my pressure, if I put my hand in front of the fan, the flow rate goes down. If I have no pressure, I have my maximum flow rate. If I increase my pressure then the flow rate goes down. What happens in this part? The same thing. In the knee, a slight increase in pressure, so from .59 to .63, reduces the flow rate quite a bit.

Stratix 10 FPGA

CFD simulations showed that the fans were operating in the “knee” where it is hard to judge the impact of pressure changes on flow rate and vice versa. (Advanced Thermal Solutions, Inc.)

“So, for a 0.1 difference in flow rate (in cubic meters per second) it took 0.4 inches of water pressure difference, whereas here for a 0.1 difference in flow rate it only took a .04 increase in pressure. That’s why there’s a circle there. It’s a danger area because if you’re in that range it gets harder to predict what the flow will be because any pressure-change, any dust build-up, any change in estimated open area might change your flow rate.

Fan Laws

CFD analysis of flow vectors across high-powered components on a PCB. This simulation was part of an examination of fan performance in a system. (Advanced Thermal Solutions, Inc.)

CONCLUSION

While it is important to know the types of fans on the market and manufacturers provide data about the power and operating ranges of each product, it is important for there to be a basic understanding of the laws that govern how fans operate in a system and an ability to examine fan curve data in order to optimize performance.

“Bulk testing of electronics chassis provides the relationship between air flow and pressure drop and determines the fan performance needed to cool a given power load. The fan rating is often a misunderstood issue and published ratings can be somewhat misleading. Knowledge of fan performance curves, and how they are obtained, allows for a more informed decision when selecting a fan. Continued and ever shortening product design cycles demand a ‘get it right the first time’ approach. The upfront use of system curves, fan curves and fan laws can help meet this goal.”

Read more and see examples of fan laws and curves in practice at https://www.qats.com/cms/2013/07/24/how-to-use-fan-curves-and-laws-in-thermal-design.

CLICK HERE FOR PART I

To learn more about Advanced Thermal Solutions, Inc. consulting services, visit www.qats.com or contact ATS at 781.769.2800 or ats-hq@qats.com.

Utilizing Fans in Thermal Management of Electronics Systems

Fans in Thermal Management

There are different types of fans that are used in thermal management of electronics with tube axial fans being the most common. (Wikimedia Commons)


The ongoing trend in the electronics industry is for increasingly high-powered components to meet the ever-growing demands of consumers. Coupled with greater component-density in smaller packages, thermal management is more and more of a priority to ensure performance and reliability over the life of an electronics system.

As thermal needs have grown, engineers have sought out different cooling methods to supplement convection cooling. While options such as liquid cooling have grown in popularity in recent years, still one of the most common techniques is to add fans to a system.

Through the years, fan designs have improved. Fan blades have been streamlined to produce great flow rate with less noise and fans have become more power-efficient to meet the desires of customers trying to use less resources and save costs.

While much has changed in the presentation of fans, there are many basic concepts that engineers must consider when deciding how to implement fans in a project.

This is part one of a two-part series on how to select the best fan for a project. Part one will cover the types of fans that can be used. Part two, which can be found at https://www.qats.com/cms/2017/03/10/analysis-of-fan-curves-and-fan-laws-in-thermal-management-electronics, will cover fan laws and analyzing fan curves.

COMMON TYPES OF FANS AND BLOWERS

As described by Mike Turner of Comair Rotron in an article for Electronics Cooling Magazine, “All You Need to Know About Fans,” fans are essentially low pressure air pumps that take power from a motor to “output a volumetric flow of air at a given pressure.” He continued, “A propeller converts torque from the motor to increase static pressure across the fan rotor and to increase the kinetic energy of the air particles.”

In a white paper from Advanced Thermal Solutions, Inc. (ATS) entitled, “Performance Difference Between Fans and Blowers and Their Implementation,” it was added that fans are at their core, dynamic pumps. The article added, that in dynamic pumps “the fluid increases momentum while moving through open passages and then converts its high velocity to a pressure increase by exiting into a diffuser section.”

The biggest difference between a fan and a blower is the direction in which the air is delivered. Fans push air in a direction that is parallel to the fan blade axis, while blowers move air perpendicular to the blower axis. Turner noted that fans “can be designed to deliver a high flow rate, but tend to work against low pressure” and blowers move air at a “relatively low flow rate, but against high pressure.”

The three types of fans are centrifugal, propeller, tube axial, and vane axial:

• In centrifugal fans, the air flows into the housing and turns 90 degrees while accelerating due to centrifugal forces before being flowing out of the fan blades and exiting the housing.
• Propeller fans are the simplest form of a fan with only a motor and propellers and no housing.
• Tube axial fans, according to Turner, are similar to a propeller fan but “also has a venture around the propeller to reduce the vortices.”
• Vane axial fans have vanes trailing behind the propeller to straighten the swirling air as it is accelerated.

The most common fans used in electronics cooling are tube axial fans and there are a number of manufacturers creating options for engineers. A quick search of Digi-Key Electronics, offered options such as Sunon, Orion Fans, Sanyo Denki, NMB Technologies, Delta Electronics, Jameco Electronics, and several more.

Fans in Thermal Management

A fan is added to a heat sink on a PCB in order to increase the air flow and heat dissipation from the board component. (Advanced Thermal Solutions, Inc.)

FACTORS TO CONSIDER WHEN PICKING A FAN

When selecting a fan, engineers must consider the specific requirements of the system in which they are working, including factors such as the necessary airflow and the size restrictions of the board or the chassis. These basic factors will allow engineers to search through the many available options to find a fan that fits his or her needs.

In addition, engineers may look towards combining multiple fans in parallel or in a series to increase the flow rate across the components without increasing the size of the package or the diameter of the fan.

Parallel operation means having two or more fans side-by-side. When two fans are working in parallel, then the volume flow rate will be increased, even doubled when the fans are operating at maximum. Turner added. “The best results for parallel fans are achieved in systems with low resistance.”

In a series, the fans are stacked on top of each other and results in increased static pressure. Unlike parallel operations, fans in a series work best in a system with high resistance.

The ATS white paper noted, “In real situations, the fans may interfere with each other. The end results is a lower than expected performance.” Turner warns that in either parallel or series configurations there is a point in the combined performance curve that should be avoided because it creates unstable and unpredictable performance, but analyzing fan performance and fan curves will be covered in more detail in part two of the blog.

Efficiency is a major factor when selecting a fan. As noted in an article from Qpedia Thermal eMagazine, “A large data center contains about 400,000 servers and consumes 250 MW of power. It has been estimated that about 20% of the total power supplied to a high end server is consumed by fans.”

Clearly, finding a fan that can work efficiently with lower power will save a considerable about of resources. The article details several methods for creating efficiency in designing a system that includes fans:

“Fan power consumption is traditionally reduced by controlling the motor speed to produce only the airflow required for adequate cooling, rather than operating continuously at full speed. Significant energy savings can be achieved beyond this technique through fan efficiency increase. Optimizing the motor and electronic driver, increasing fan aerodynamic efficiency through careful redesign, and optimizing fan-system integration are three ways of achieving this.”

Read more about the techniques for achieving efficiency at https://www.qats.com/cms/wp-content/uploads/2015/03/Designing_Efficient_Fans_for_Electronics_Cooling
_Applications.pdf
.

CLICK HERE FOR PART II.

To learn more about Advanced Thermal Solutions, Inc. consulting services, visit www.qats.com or contact ATS at 781.769.2800 or ats-hq@qats.com.