Robin Bornoff's blog

Views and insights into the concepts behind electronics cooling with a specific focus on the application of FloTHERM to the thermal simulation of electronic systems. Investigations into the application of FloVENT to HVAC simulation. Plus the odd foray into CFD, non-linear dynamic systems and cider making.

24 February, 2015

It’s National Engineer’s Week in the US this week. A similar event takes place in the UK in March. I’ve been lucky enough to have been an engineer (mechanical) for my entire career and long may it continue! However I didn’t start out enthusiastic about engineering at all. When I was young I never really had an interest in renovating cars, fixing engines or taking things apart generally. I was best at mathematics and physics which was why I defaulted to studying mechanical engineering at university. It was there that my love of a specific type of picture drove my love of the subject.


Vicotrian_engineering_genius_isambard_kingdom_BrunelMand1Brunel was the most famous of Victorian engineers. Designer of bridges, railways and big big ships, the University that bears his name lies just to the west of London. 1988 was a good time to start at Brunel. At that point it was still fully government funded so we did have certain freedoms not enjoyed today. It was in my first year that I first saw a fractal, a real ‘wow’ moment. I was keen to find out more.

Mathematics is to Engineering as words are to poetry. Understanding the math(s) behind how a fractal is generated was a revelation. So much complexity, at an infinite number of scales, from the simplest of non-linear relationships. The universe’s artwork indeed. Spurred on to find out more I next came across the Lorenz attractor.

A_Trajectory_Through_Phase_Space_in_a_Lorenz_AttractorIn 1963 Ed Lorenz published his seminal paper “Deterministic nonperiodic flow. Journal of Atmospheric Sciences. Vol.20 : 130—141″. The butterfly effect was a term coined from it. Put simply, a (non-linear) mathematical model that intended to predict global weather patterns was found to be sensitive to initial conditions. Start two simulations with very very slightly different starting points and the two solutions will, after a while, deviate widely from each other. How fascinating and fundamental is that!

RaBeWhen I did specialise in fluid dynamics during my final couple of years at Brunel I was naturally motivated to understand the concepts behind Lorenz’ study. Unlike some of my fellow students I couldn’t wait to find out about boundary layer theory, turbulence, turbulence modelling and computational fluid dynamics (CFD). Once I had enough theory, I did study Lorenz’ paper in detail. His model was akin to a Rayleigh-Bernard convection configuration, hot floor, cold ceiling, the air circulates between the two, hot air rises, colder air sinks back down. The Lorenz attractor is simply a graphical representation of state of those convection cells, their rotational rate, the temperature difference across them. For some more extreme temp differences between hot floor and cold ceiling those convection cells spin one way, then spin another, in an a-periodic way. That point flying round the attractor NEVER crosses its own path, the system is random, chaotic and ultimately unpredictable. The displayed animated CFD simulation of such a convection can be considered as a single point on the attractor. It’s purely coincidental that the attractor and convection cells look somewhat similar!

After uni, infused with a love of CFD, I was extremely fortunate to go work for Flomerics, a UK CFD software vendor focusing on simulating HVAC and electronics thermal applications. I’ve been there ever since (now part of Mentor Graphics). Developing and demonstrating the value of CFD, surely engineering jobs don’t get much better than this!

Generic Laptop Section Top  Window_and_radiator   Air flow over heatsinks

There are no stereotypes in engineering. You don’t have to be into cars to excel in mechanical engineering, into stripping down radios to be a brilliant electrical engineer. All you need is a good grounding in math(s) and a love and enthusiasm for understanding the world around you.

24th Feb 2015, Ross-on-Wye

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10 February, 2015

A full 3D thermal simulation of an electronic system requires, not surprisingly, a 3D geometric representation of the proposed design. Much of the design data for a phone, laptop, blade server, IGBT cooling system etc. often already resides in an MCAD system and is readily importable into FloTHERM or loadable into FloTHERM XT. EDA design data, be it a PCB layout or BGA substrate, is often held in 2 or 2.5D descriptions. The need to convert that data into a 3D thermal representation has led us to evolve a couple of unique FloTHERM technologies.


PCB_Patch_ResolutionDirect interfaces (installed with FloTHERM) to Expedition, BoardStation and other EDA tools extract all the necessary information required for an efficient yet thermally accurate definition of the PCB/Substrate. The complexity of modern day PCB designs, as is evident from the winners of Mentor’s annual Technology Leadership Awards, is such that it is intractable to extract the EDA data as 3D at source. Instead we have pioneered a ‘light’ approach whereby all metallic routing/via information on conducting and dielectric layers is rendered to a high resolution raster image. When further compressed, the data is a fraction of the size it would otherwise be, without losing any of the fidelity required to reconstitute a detailed thermal representation. To do that, a real-time graphics processing technology is used in FloTHERM to convert the images to a thermal conductivity map. The resolution is slider-bar controlled, allowing an appropriate detail to be achieved. For forced convection applications, where most of the heat does not go into the PCB, a lower resolution can be used. For natural convection or conduction cooled environments a much higher resolution can be used that accurately resolves the patch-by-patch orthotropic conductivity thermal resistances on each PCB layer.

[As an aside, here’s an interesting whitepaper on 10 tips for streamlining PCB thermal design]

An alternative technology can also be used to convert any image (regardless of source) representing a metallic distribution into an extruded identical 3D representation. Here no orthotropic pixelated patches are created, a single object with an assigned material can be created that is the 3D (extruded) equivalent of the 2D image.

Just by obtaining some images of the Cu distribution in a BGA substrate a 3D model may be constructed and thermally simulated.

BGA_Substrate

FloTHERM_MandlebrotI’m a big fan of fractals. So, to exercise this functionality further I converted a picture of a part of the Mandelbrot set into a 3D extruded solid, made it out of Copper, embedded in a lower conductivity substrate and imposed a 100degC temperature difference across it and simulated the resulting heat flow. I’ve yet to figure out a useful application for this but hey, it looks all kinds of stunning.

MagHeatFlux

HeatFluxAnim4

Not content to stop there (OK, I got carried away) I also simulated the hot spur of a well known footballing chicken (COYS!!)COYS

And finally, as Valentine’s day approaches, and as is representative of the love I have both for my wife and for this functionality, the thermal behaviour of a dissipating heart (6W, k=385W/mK embedded in a 2W/mK substrate, peripheral HTCs of 1000W/m2K (top) and 100W/m2K (other sides) @0degC), based simply on an image LoveHeart_small  was also simulated in FloTHERM.

LoveHeartFloTHERMModel

Copper Heart Encased in Plastic

HotLove

Temperature Distribution from the Dissipating Heart. That’s Some Hot Love.

Valentines

Animated heat flux vectors (note the heart’s thermal stagnation point)

Happy Valentine’s day everyone. May your day be filled with love :)

10th February 2015, Ross-on-Wye

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21 January, 2015

Energy isn’t the only thing that is wasted when dealing with hot water supply. Time is as well. The house I live in is nearly 200 years old. It wasn’t a very well made house when it was new and, unlike a good wine, has not got better with time. The retrofitted, refitted, repaired, repaired and repaired again hot water supply system would have benefited from some up front design. As it is the flow rate of the hot water to the taps (faucets) is as tardy as it is tepid. #FirstWorldProblem, sure, 750 million don’t have access to a clean source of water, let alone a clean source of preheated water. Regardless of application, getting water from A to B has been a human endeavour for millennia. Fluids simulation helps you do it better.


Fat Finger SensorTurn the hot tap on in my kitchen and you have to wait ages for the dribbly stream of water to become hot. Despite knowing this and being reminded of it every day, I still deploy the FFS (fat finger sensor) every time I turn the hot tap on. That’s about 80 seconds of time wasted, just standing there, staring at my finger, waiting. A daily ritual that often involves my beloved hollering “You might as well put the bins out instead of just standing there!”.

The flow is so low because it’s a gravity fed (and ill-designed) system. A header tank above feeds water to the hot water tank below. There is a then a long run of 15mm diameter plastic pipe to the kitchen sink. The header tank is quite shallow and only a metre or so above the hot water tank. Not much head to force the water out through the ~23m run.

Mentor Graphics acquired Flowmaster 3 years ago, adding it’s ‘1D CFD’ systems simulation capability to the suite of simulation tools offered by the Mechanical Analysis Division. For large fluid dynamics systems, dominated by pipe delivery, 3D CFD is often too computationally inefficient to be deployed. 1D CFD offers a network based approach to solving for fluid flow and heat transfer in a fraction of the time, allowing pump/pipe sizing to be determined early in the design process.

HotWaterSystemSketchPersonally I’m just beginning to get familiar with the use of Flowmaster, so for this study I cajoled my colleague Doug Kolak into simulating my hot water system (thanks Doug!). In the absence of a CAD description I sketched out my configuration for him. And yes, after years of constant keyboard use my drawing skills have regressed to that of an 8 year old :(

A flow actuated pump would be a beneficial addition to the system. Forcing the water through faster than gravity alone manages. Flowmaster can easily be applied to simulate both configurations, giving an indication of what advantage the pump offers in terms of how long to wait until the water supply heats up and the subsequent sink bowl filling time. The animation shows both configurations, overlayed. Interesting to see how the ‘front’ of hot water becomes diffused leading to a gradual increase of temperature at the tap.

Overlay

TankSinkThe Flowmaster network is constructed from a number of different components, pre-characterised for their fluid and thermal behaviours. Tanks, pumps, pipes, diffusers, jet pumps, bends, T-junctions, Y-junctions… with over 400 to select from a wide range of applications are covered. Despite the relative simplicity of this model, the control system at the sink end is quite interesting, set to start filling the sink bowl once the temperature of liquid coming out of the tap rises over 40degC (equating to me moving the tap over a plastic bowl in the sink when FFS starts to get too hot), then the tap is turned off when the bowl is filled to 99% of its volume.

Water Temperature at TapInclusion of the pump shortens the time it takes for the water at the tap to get warm by about a half, the bowl takes half the time to fill and there is a slight increase in the temperature coming out of the tap (due to there being less time for the water to lose heat as it flows through the pipes, from the tank to the sink).Sink Percent Full Any further improvement might be had by increasing the diameter of the pipe so that the pump would work at a higher flow rate. In fact the design challenge could be inverted so that the required time to fill the sink might be defined as an input to the model, the sizing of the components of the hot water system then be determined through simulation so as to achieve that design goal. With simulation time measured in seconds, the way in which Flowmaster can be deployed offers many advantages over classical 3D CFD.

Flowmaster is available for evaluation using our ‘Virtual Lab’ technology. No need to install locally, just log in and get going!

21st January 2015, Ross-on-Wye

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9 January, 2015

Horror vacui, Natura abhorret vacuum, Resintenza del vacuo; from Aristotle to Galileo, it has long been known that here on earth matter tends to flow from where it is to where it isn’t. Fast forward to Hobbes and Boyle, Newton and Leibniz and we find such concepts being being applied in a fluid dynamics context. From a heat transfer perspective heat energy also tends to flow from where it is to where it isn’t, from where it is hot to where it is cold. An equilibrium will be reached. As humans we love energy, we can do lots of interesting things with it. We constantly endeavor to get all those Joules to do our bidding, including storing them for later use, which is many ways can be like trying to corral cats.


So it took a couple of hours to heat (a point in) the water tank to 60degC. As sure as eggs those Joules aren’t going to hang around. They will leak out and in their wake they will  leave the water at the same temperature as the ambient in which the water tank sits. Let’s take a qualitative look at how insulation surrounding the tank plays a role in delaying the inevitable.

I personally don’t have a material thermal conductivity tester to hand (but sure, we do offer one in the form of our DynTIM). So far in this study I’ve assumed the insulation on my tank is expanded polyurethane type foam with a very low conductivity of 0.03 W/mK, that’s about 6000 times better at keeping the heat in compared to Aluminium. What if it wasn’t as good an insulator? What if it was say, 0.24 W/mK about that of cotton. Let’s compare and contrast how the heat leaves and the water cools when using these different insulation materials. Good insulation on the left, not so good on the right, the temp range is from 25 to 62 degC. The simulation simply carried on from the end of the warm-up, but with the power to the heater element turned off and the resulting change in temperature simulated over the next 10 hours:

TemperatureCoolDown

The water at the top of the boiler certainly remains hotter for longer which is why the hot water extract pipe is always higher up in the tank. Seen even more clearly if we look at how the volume of water that is greater than (an arbitrary) 54.5 degC shrinks over that time:

54p5DegC_isosurface

TeaCosySo, from a design perspective, having understood where the heat is leaving you could consider investigating shifting some of the insulation to the top of the tank, where the energy in the hot water tends to gather. There’s always room for improvement. Actually not always, a tea pot with a tea cosy on is as perfect an insulated object as you’ll ever find and although it might be improved from a thermal perspective, its quintessential Britishness is timeless :)

9th January 2014, Ross-on-Wye

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19 December, 2014

Sure, most modern homes have much more advanced water heating systems than a big tank with an electric heater element. Systems that work on ‘point of use’ heating concepts that heat the water as it is being delivered, not heating up a large storage container for later use. One day I hope to live in such a house! Until then I’m going to worry and fret about the amount of money spent on heating water and subsequently the amount of money lost as that energy leaks away. So, can simulation predict how much money it takes to heat a water tank? Well yes!


Hysteresis Thermostatic ControlBasic thermostatic control approaches relate the amount of (heating) power to the difference between a set point temperature and the temperature of the system being heated. This type of proportional control can be further enhanced to also take into account the rate at which the temperature is changing (derivative) as well as some history of what how the system has changed up to now (integral). Such PID control systems require constants that need to be tuned to achieve the most efficient approach to ensuring the set point temperature is achieved without under or over shoots. For this study I’ve adopted just a proportional control but including the effects of hysteresis using standard transient features in FloTHERM.

A maximum power (in this case 2000W) is derated once a nominated point in the tank exceeds 55 degC. Once it exceeds 60degC the power is limited to 100W. If the temperature starts to drop then a different curve is used to determine the required increase in power. This offset between the two is intended to stop rapid changes to the power as the water temperature tends towards its intended set point.

Power Temperature Warm UpThe FloTHERM transient simulation covers a period of about 2 hours, during which the temperature is seen to stabilise at ~60degC. It takes just over an hour to settle down to this value. The changing power over that time is also stored.

Power is energy per time. The integral of the power history is the amount of energy (Joules) used. We pay for Joules, Joules = $£. About 7,800,000 Joules are used to raise the water temperature from 25 to 60 degC.

Does that sound about right? There’s a simple hand calculation that can also be used to double check: This 60 litre tank contains 60kg of water. The specific heat of water is ~ 4200 J/kgK. It takes 4200 Joules to raise 1kg of water 1 K (a 1 Kelvin increase is the same as a 1 degC increase). So, it will take 60*4200*(60-25) Joules to raise the entire tank to the required level. This works out at 8,820,000 Joules.

A bit higher than the simulation predicts. Why might that be? Let’s have a look at the temperature distribution within the tank during warm up:

WarmUpTemps

The red dot is the point at which the water temperature is measured. As can be seen there is quite a variation in water temperature, it does not heat up equally throughout. As is often the case with such simple approaches, the hand calc used above assumes that the water heats up uniformly, just as doesn’t happen in reality. The 11% or so difference between hand calc and 3D CFD simulation is due to the fact that not all the water is heated to achieve the 60degC at the single thermostat control point, less energy is required. You can see a pool of much colder water at the bottom of the tank.

A good example of the value of 3D simulation over simpler assumption prone equations.

Next time we’ll look at how good the tank is at holding onto those 7.8 million Joules.

19th December 2014. Ross-on-Wye.

(p.s. tagged with ‘electronics cooling’ as for sure the water is doing its best to cool the electrically heated element just as much as the element is battling to heat the water :) )

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15 December, 2014

Human technical prowess relies heavily on the conversion of energy from one form to another, to meet our needs. Physiological needs underpin  Maslow’s pyramid of all our other ones. The availability of hot water is in turn important for that. Likely we’ve been heating water since the early Neolithic, converting chemical energy stored in wood to heat (radiative and convective) which in turn raises the temperature of a vessel of water. Despite some forays to the moon and a landing on a comet, the intervening years of human endeavour have seen little evolution of this water heating approach.


Temp and Outside FlowWhat is considered socially acceptable has changed much in the last few decades. Smoking was commonplace on TV and in film up to the 80s, today such on-screen behaviour is rare and roundly condemned. I wonder what will be considered similarly abhorrent in 40 years time? May well be wastage generally, for energy especially.

“Look Dad, look at them on holo-vid heating their water in a tank and leaving it there to cool down before they even used it. Yuck!”

EnergyUsageSunnovations, hopes to address hot water heating energy wastage through a kickstarter supported product called Aquanta. It’s neat in that it will learn when best to heat the water so that energy is converted as close to the time of use as possible. Limiting how those Joules you add might otherwise float away over time, like money in a casino. The ability to compare your energy efficiency with your peers might well be the start of a social revolution in acceptable behaviour.

Simulation plays a critical role in predicting the energy efficiency of a proposed boiler tank design. Be it the cost effectiveness of lagging, the balance between a hot enough temperature to achieve pasteurization of for example Legionella pneumophila but not so hot as to scald, the time it takes for the temperature in a water tank to drop back down to ambient, all of these and maybe more I’ll investigate using the CFD (computational fluid dynamics) simulation capabilities of our tools such as FloTHERM, FloEFD and Flowmaster.

Steady State Boiler Flow15th December 2014, Ross-on-Wye

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25 November, 2014

A lot of engineering involves the management of energies, converting them from one form to another, channeling off useful work to power our cars, planes, phones, lights etc. To date we haven’t come up with a system to channel all energy into a desired task. Even the most advanced power generation systems still end up heating water to steam to drive turbines, a cycle that is no more than about 38% efficient. The rest of the energy mostly lost to heat. Electronics are no different. A lot of power is consumed just to move electrons and photons about. That useful work doesn’t require much energy (what’s the kinetic energy of an electron?), nearly all the input energy is lost to heat in the process. If you’re going to lose it, why not use it?


Electronics dissipate energy as heat power and thus get hot. Too hot and they start to malfunction then ultimately fail completely. Getting the heat out as fast as it’s generated keeps the temperatures down. This is at the heart of electronics thermal management. Tools such as FloTHERM were designed to help simulate such heat transfer and resulting temperature levels. The heat still has to end up somewhere, usually an assumed ‘ambient’. A pool of cool so big that it tends to keep at just about the same temperature even when heat is dumped into it. A good enough engineering assumption most of the time. However if you want that ambient to heat up then the engineering challenge of dealing with waste heat becomes an engineering opportunity!

qrad

Qarnot Computing Q.rad Computer Architecture

A French company, Qarnot Computing,  provides a computer called Q.rad that is designed to be a room heater, provided as part of a cost effective distributed HPC cloud deployment system.  Their HPC customers are linked to their heating customers so that HPC demand is automatically distributed to the Q.rad farm. An ingeniously green approach, in recognition of the inefficiencies that blight our modern world.

Until such time as a room temperature superconductor is developed (http://bestsf.net/greg-egan-zero-for-conduct-twelve-tomorrows/), or we all go live inside a mega data center, we will be faced with the more pressing challenge of determining where to place Q.rad in our home to ensure efficiency from the heat leaving Q.rad to it reaching your extremities. Keep warm everyone!

Window_and_radiator

25th November 2014, Ross-on-Wye

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28 October, 2014

There are 475,000 Google search hits for ‘thermal bottleneck’. It’s a well recognised phrase playing on a very obvious analogy. Despite this there were few attempts to quantify such a parameter. Spurred on by its relevance in electronic thermal design, we did just that, put hard numbers to the concept. FloTHERM now has the ability to plot distributions of  thermal bottleneck, enabling thermal design engineers to predict not just how hot a product might become in operation, but also the culprit locations that cause that temperature rise.


When determining how to quantify a thermal bottleneck we knew that it should combine both the amount of heat that flows as well as the difficulty the heat experiences as it flows. The amount of heat flowing is simply the heat flux, W/m^2. The difficulty was a little more difficult to figure out. In the end we selected the temperature gradient as being indicative of the difficulty. The bigger the temperature difference, the more the difficulty experienced that gave rise to that dT/dx. Both are vector quantities. How best to combine both? (a lot of heat flowing AND finding it difficult). Dot product seemed best. Finally normalised by its maximum to show the relative distributions in a single simulation model.  And that was the invention process, full details can be found in US patent US8628236.

Take a section through a typical FloTHERM application (in this case a wall mounted repeater unit):

Hot_and_Why

On the left is temperature. On the right is thermal bottleneck, Bn. Showing clearly where the bottlenecks are makes for a compelling demonstration as to why design changes might be needed, and where. Much design work comes down to influence and balance. Having such a Bn capability is a useful influencing tool for the thermal design engineer.

Analogies have always played an important role in the evolution of science and technology. As Einstein said: “Growth comes through analogy; through seeing how things connect, rather than only seeing how they might be different.” It was therefore rather gratifying when we discovered that the thermal bottleneck is analogous to the Joule heating power density in a DC current flow (current density x voltage gradient). W/m^3 compared to the thermal bottleneck degC W/m^3. If Joule heating is the energy required to force the current through where its flowing, then bottleneck is the energy required to get the heat over a 1 degC hurdle. Neat.

You can find out more details in this webinar I gave on this subject.

28th October 2014, Hampton Court.

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21 October, 2014

Lighting accounts for ~20% of the world’s total energy consumption. This is a staggering statistic. Why so much? Historically a lot of energy had to be consumed to produce the required amount of visible light. Incandescent bulbs, where electric current is used to heat a metal filament, resulted in most of the energy being dissipated as heat. More a hot bulb than a light bulb. Even worse for a candle. LEDs mark a step change in the efficiency by which energy is converted into light, they’re cooler as a consequence and thus more reliable. Win/win/win. Their ubiquity today is due to the breakthrough in creating an LED capable of emitting blue light (red and green having already been achieved). With the ability to create those 3 colours, they could be combined to produce white and all colours in between. The 2014 Nobel prize in Physics was awarded to 3 scientists who ‘invented’ the blue LED in the 1990s, despite the fact that a 1974 US patent indicates otherwise.


A Lumen (lm) is a measure of the energy contained in that part of the EM spectrum that your eye can sense, the visible spectrum. A luminous ‘efficiacy’ of radiation is a measure of how much visible light power there is compared to the power in all frequencies of the light. More the better. LEDs are on their way to becoming x1000 better than historic forms of lighting:

Lumens_per_Watt

Ironically, LEDs perform better if they are cooler. At higher (junction) temperatures the brightness decreases, the emitted frequency shifts and the reliability is compromised as well. Good thermal management, in terms of the physical characterisation of their optical and thermal properties as well as a simulation based design methodology, is essential. The LED vendor market is coalescing through acquisition, maintaining a competitive product edge is as important as it ever was. Such thermo-optical applications are a real sweet spot for the technology in the Mechanical Analysis Division of Mentor Graphics. The MCAD embedded CFD technology of FloEFD coupled with the T3Ster and TeraLED physical test and measurement equipment is especially well suited to automotive lighting applications where the use of LEDs is now commonplace.

Cluster_LEDsAn interface between T3Ster and FloEFD enables temperature dependent LED optical properties, measured by TeraLED, to be imported into FloEFD whereupon a ‘hot lumen’ prediction can be simulated based on a specified driving current. That, coupled with a choice of 3 optical radiation models in FloEFD and the ability to handle complex MCAD assemblies directly within the MCAD environment, make for a unrivalled industrial strength capability.

W_Rhines

Wally Rhines, CEO, Mentor Graphics

One of the authors of the overlooked 1974 US patent “Gallium nitride metal-semiconductor junction light emitting diode was Wally Rhines, CEO of Mentor Graphics. Wally is one of those rare captivating keynote speakers who can take you on a roller-coaster of a ride into the world of EDA and deposit you at the end feeling intrigued yet satisfied. It would have been cool to have a Nobel Laureate as CEO. It’s a bit of a shame it didn’t work out that way this time.

21st October 2014, Ross-on-Wye.

p.s. Wally’s not only a keynote speaker of course, his CEO track record at Mentor speaks for itself. Thought I’d clarify that in case he stumbles on this blog… :)

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6 October, 2014

“How thick is a leg hair” is not a question I thought I’d be posing when I woke up today. Reading this article about the effect of leg hair on the overall drag of a performance cyclist prompted me to investigate the physics behind this observation. For the sake of expediency I steered clear of a full body MAMIL simulation, focussing instead on just a small patch of a particularly hairy leg subjected to an oncoming air flow.


BoundaryLayerAir will ‘stick’ to any solid surface, even a silky smooth leg. Away from the wall it speeds up until it reaches free stream speeds. That thin sliver of slow moving air, hugging each solid surface, is called a boundary layer, often graphically indicates as a series of velocity vectors, running parallel to a wall, increasing in size away form it. This stickiness offers a resistance, more for treacle, less for air. So, even a shaved leg will offer some resistance to the air flow.

HiarDistributionAdd to the wall (skin) a whole forest of hairs then you’ve added loads of new solid surface area to which the air will stick. I did go looking for a MCAD file of hair but to no avail. Instead I mocked up a random distribution of stubble type hairs of a density pretty high up the Chewbacca scale. Nice and simple, no moving hairs, no curly hairs, no solid fluid interaction. Such stubborn stubble being an extreme case in cycling aerodynamic drag, rather lack of it.

In addition to the increase in surface area and the resulting increased stick of the fluid, the more tortuous a path the fluid has to travel through the hairy obstruction, the more difficult it finds it to do so. This fluid difficulty manifests itself as a pressure drop, effectively the energy required to achieve that flow.

SpeedBetweenLegHairsSo, using FloTHERM (though FloEFD would have been more appropriate, old dog, new tricks etc.), I simulated air approaching the segment of hairy skin to see just how tortuous a path it would be made to follow. The red areas are high speed flow, the blue/green areas low speed, especially obvious in the slow moving recirculating wakes behind each hair. Even more evident if we look at the flow animation:

leghair

Each twist and turn of the flow equates to a force required to overcome it. The more hairs you have on your legs, the more force you’ll need to overcome it, the more energy you’d need to expend. For Chewbacca this equates to the difference between changing from a round-tube frame to an aero-style one.

FloEFD has a proven track record for sports applications as this fascinating webinar by Olympian Professor Kristan Bromley shows.

6th October 2014, Ross-on-Wye

p.s. Diameter of a human hair is between 17 and 180 microns (millionths of a meter). For the case above I chose a particularly hirsute 200 microns (0.2 mm).

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Meet Robin Bornoff

With a mechanical engineering background and CFD foundation, I have 20 years of experience in the field of electronics cooling design and simulation. Beyond my vocation I enjoy making my own cider, appreciating fractals and prime numbers, running (slowly) and will only ever read Sci-Fi.

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