In this video, we show how to attach heat pipes into an assembly. This 8-minute video covers: – Mechanical press fit, thermally conductive epoxy, and soldering – Grooving the base – Some best practices
Learn 3 ways to attach heat pipes to an assembly by clicking the video to see it on our Youtube channel
==> ATS’ large offering of heat pipes includes 350+ round and flat heat pipes, you can learn about that on our web site at https://www.qats.com/Products/Heat-Pipes
This graphic shows one application for heat pipes in renewable energy, from “A Review of Heat Pipe Application Including New Opportunities”, in Thermal Fluids Central,
While this paper “A Review of Heat Pipe Application Including New Opportunities”, in Thermal Fluids Central, was published in 2011, authors Masataka Mochizuki*, Thang Nguyen, Koichi Mashiko, Yuji Saito, Tien Nguyen and Vijit Wuttijumnong’s work is applicable today as it was then. This is especially true of section 5.1 as it relates to the use of heat pipes in renewable energy where they site a number of helpful ideas. Whether you are new or seasoned in the use of heat pipes, this paper is a helpful resource. A key tool for every thermal engineers tool kit. You can download the PDF here for no cost from Thermal Fluids Central: https://www.thermalfluidscentral.org/journals/index.php/Heat_Pipes/article/view/127/200
New to heat pipes? Let me recommend two additional resources:
Heat pipes and vapor chambers are often lumped into liquid cooling, but, they are not actuallycooling they are in fact moving heat from a hot location to another location where it is dissipated. And how they operate, how to choose them, and how to deploy them is a very important part of the thermal engineer’s toolbox. And we have a free webinar on this topic to help train you.
When is it best to use a heat pipe and when is it best to use a vapor chamber?
The webinar will be recorded, and for those who register, we will provide the recording for your review.
While the webinar lasts about an hour, we’ll have about 30-minutes for a Q&A afterward. Don’t delay, sign up today at the button below! NOTE: this webinar had been scheduled for 10/15 but due to scheduling conflicts, our team needed to move it to 10/22.
Have questions about heat pipes and vapor chambers? email us at ats-hq@qats.com and we’ll be happy to answer them and direct you to the right solution.
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]
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]
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.
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]
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.
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.
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.
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.
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.
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]
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.
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.
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.
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]
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 [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.
“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.
Most blade
servers for data and telecommunication systems use air to cool the high-power
chips inside. As the power level of these chips keep increasing, the pressure
is on thermal engineers to design ever higher performance air-cooled heat
sinks. In recent years, advancements in manufacturing of thinner heat pipes and
vapor chambers have enabled engineers to integrate the heat pipes and vapor
chambers into the blade server heat sinks.
A heat pipe
is a device with high thermal conductance that can transport large amounts of
heat with a small temperature difference between its hot and cold ends. The
idea of a heat pipe was first proposed by Gaugler [1]. However, only after its
invention by Grover [2, 3] in the early 1960s, were the remarkable properties
of heat pipes realized by scientists and engineers. It is now
widely used to transport heat from one location to another location or to smooth
the temperature distribution on a solid surface.
A heat pipe is a self-driven two-phase heat transfer device. A schematic view of a heat pipe is shown in Figure 1. At the hot section (evaporator), the liquid evaporates and turns to vapor. The vapor flows to the cold section (condenser) and liquefies there. The liquid is driven back from the cold section to the hot section by a capillary force induced by the wick structure. By using boiling and condensation, the heat pipes can transfer and spread the heat from one side to another side with a minimum temperature gradient.
Figure 1. Typical heat pipe. [4]
Vapor chambers are flat heat pipes with very high thermal conductance. They have flat surfaces on the top and bottom sides. See Figure 2, which can be easily attached to a heat source and a heat sink.
Figure 2. Vapor chamber. [5]
Just
like heat pipes, vapor chambers use both boiling and condensation to maximize
their heat transfer ability. A vapor chamber generally has a solid metal
enclosure with a wick structure lining the inside walls. The inside of the
enclosure is saturated with a working fluid at reduced pressure. As heat is
applied at one side of the vapor chamber, the fluid at locations close to the heat
source reaches its boiling temperature and vaporizes. The vapor then travels to
the other side of the vapor chamber and condenses into liquid. The condensed
fluid returns to the hot side via the gravity or capillary action, ready to
vaporize again and repeat the cycle.
In
electronics cooling, heat pipes are generally used to move the heat from
electronics to heat dissipation devices. For example, in a desktop computer,
multiple heat pipes are used to transfer heat from a CPU to an array of cooling
fins, which dissipate the heat to ambient environment through convection. Vapor
chambers are generally used to spread heat from a small size device to a larger
size heat sink, as it is shown in Figure 2. If used in server heat sinks, the
heat pipes and vapor chambers are both used to spreading the heat due to the
low profile and large footprint of the heat sinks.
Compared
to copper heat spreaders, heat pipes and vapor chambers have the following
merits.
First,
they have a much higher effective thermal conductivity. The pure copper has a
thermal conductivity of 401 W/m°C and the best conductive material of diamond
has a thermal conductivity of 1000-2000 W/m°C. The effective thermal
conductivity of a well-designed heat pipe and vapor chamber can exceed 5000 W/m°C,
which is an order of magnitude higher than that of pure copper. Second, the
density of the heat pipe and vapor chamber is much lower than that of copper.
Due to its hollow structure, the heat spreaders made by vapor chambers are much
lighter than those made of copper. These properties make them the ideal
candidate for high heat flux and weight sensitive heat spreading applications.
Dynatron Corporation is an electronic cooling provider specializing in heat sink for servers. This article compares the thermal performance of its server heat sinks, some of which have integrated vapor chambers. Figure 3 shows the photos of two Dynatron 1U passive server heat sinks for Intel’s Sandy Bridge EP/EX Processors. The R12 is made of pure copper with skived fins. The R19 has a vapor chamber base and stacked copper fins. The heat sink specification is listed in Table 1. The R19 is 150g lighter than the R12.
Figures
4 and 5 show the thermal performance of R12 and R19 at different flow rates. At
10CFM, both heat sinks have a thermal resistance of 0.298ºC/W. When the flow
rate increases to 20CFM, the R19’s thermal resistance is 0.218ºC/W, which is
0.020ºC/W lower than that of R12.
Figure 6 shows the photos of two Dynatron 1U active server heat sinks for Intel’s Sandy Bridge EP/EX Processors. The R18 is made of copper with skived fins. The R16 has vapor chamber base and stacked copper fins. Both heat sinks use same blower. The heat sink specification is listed in Table 2. The R16 is 90g lighter than the R18.
Figure 6. Dynatron active heat sinks R18 (left) and R16 (right). [6]
Table 2. Dynatron active heat sink specification. [6]
Figures 7 and 8 show the thermal performance of R18 and R16 at different blower speeds. At 3000RPM, the R18 and R16 heat sinks have thermal resistance of 0.437ºC/W and 0.393ºC/W, respectively. When the blower speed increases to 6000RPM, the R18’s thermal resistance is 0.268ºC/W and the R16’s thermal resistance is 0.241ºC/W. The R16 is constantly able to outperform the R18 at different blower speeds and its thermal resistance is 10% lower than R18.
Figure 7. Dynatron R18 heat sink performance. [6]
Figure 8. Dynatron R16 heat sink performance. [6]
The
comparison of the Dynatron heat sinks shows that heat sinks with vapor chambers
have a slight thermal edge vis-a-vis its copper counterparts even though they
are light. This is true for both passive and active heat sinks.
Glover et al., for Cisco, have tested different heat sinks either with embedded heat pipes or vapor chambers for their servers and published their findings [7]. They tested five different heat sinks from different vendors, who utilized different manufacturing technologies to fabricate the heat sinks. The five heat sinks are similar in size: 152.4 x 101.6 x 12.7mm. Table 3 summarizes the physical attributes of these five heat sinks.
Figure 9-11 shows the three vapor chamber heat sinks with different vapor chamber structures and fin designs. Heat sink A-1 is an extruded aluminum heat sink with a vapor chamber strip. The 40 mm wide vapor chamber strip is embedded in the center of the base. It is the lightest one among five tested heat sink. Heat sink B-1 and C-1 have full base size vapor chamber and aluminum zipper fins.
Figure 9. Heat sink with vapor chamber A-1. [7]
Figure 10. Heat sink with vapor chamber B-1. [7]
Figure 11. Heat sink with vapor chamber C-1. [7]
Figures 12-13 show the two heat sinks with embedded heat pipes. Heat sink C-2 has heat pipes embedded inside its aluminum base. It uses zipper fins and has a copper slug in the middle of the base. Heat sink D-1 has three flat heat pipes embedded in its base. It has a copper plate as base.
Figure 12. Heat sink with heat pipes C-2. [7]
Figure 13. Heat sink with heat pipes D-1. [7]
Glover et al. tested the five heat sinks at different mounting orientation and air velocity. Table 4 presents the summary results of the heat sinks at 3m/s approach air velocity. The tested heat sinks were mounted horizontally with heater sources underneath the heat sink bases.
Table 4. Heat Sink Performance at 3 m/s with horizontal mounting position and bottom heating. [7]
The C-1 heat sink has the lowest thermal resistance; thus, its values are used as the benchmark for other heat sinks. The performance of heat sinks is purely design dependent. For vapor chamber heat sinks, the thermal resistance value varies from 0.19 to 0.23°C/W for 30 W of power. For heat sinks with heat pipes, the C-2 heat sink has a thermal resistance of 0.23°C/W, which matched with that of A-1 and B-1.
The D-1 heat sink has the highest thermal resistance, which is the result of inferior design and manufacture. However, the D-1 heat sink still has relatively low thermal resistance when it is compared to a regular heat sink without a heat pipe and vapor chamber.
Figure 14 shows the thermal resistance of the five heat sinks for 60W of input power at different air velocities. The C-1 heat sink performs best for all velocities and the D-1 heat sink’s performance is the worst.
Figure 14. Heat sink thermal resistance at 60 W. [7]
The pressure drop across the heat sink at different air velocities was also measured and the results were plotted in Figure 15. The B-1, C-1 and C-2 heat sinks have similar fin structures. Therefore, their pressure drop is similar, too. The pressure drop of the A-1 and D-1 heat sinks are similar and higher than the other heat sinks. This is because the A-1 heat sink has thicker fins and the D-1 heat sink has a thicker base.
Figure 15. Heat sink pressure drop. [6]
Because the heat pipes and vapor
chambers use capillary force to drive liquid back from the condensation section
to the evaporation section, their thermal performance is prone to orientation variation.
Glover et al. also investigated the effects of the mounting orientation on the performance
of the five heat sinks. They found the effect of the orientation is design
dependent and is the result of both the wick structure and the entire heat sink
assembly construct.
The heat sink specification from
Dynatron Corporation and the test results from Cisco, show that the server heat
sinks with embedded heat pipes or vapor chamber have a better thermal
performance than their copper counterparts. The heat sinks with embedded heat
pipes or vapor chamber are also lighter than the pure copper heat sinks, which
make them more suitable for applications which are weight sensitive. If the
cost of such heat sinks is justified, they are definitely good candidates for
server cooling applications.
References
Gaugler, R. S., US Patent 2350348, Appl. 21 Dec, 1942. Published 6 Jun. 1944.
For more information about Advanced Thermal Solutions, Inc. (ATS) thermal management consulting and design services, visit https://www.qats.com/consulting or contact ATS at 781.769.2800 or ats-hq@qats.com.
