Best practices for measuring thermal properties of fluids

Dr. Cobos gives practical considerations to improve the quality of fluid measurements.

In this presentation, Dr. Doug Cobos discusses theoretical limitations of the transient line heat source technique, which define the type of fluids that can be measured and which thermal measurements can be performed with the KD2 Pro (now the TEMPOS). After discussing limitations, Dr. Cobos gives practical considerations to improve the quality of fluid measurements.

Next steps


Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum.


Dr. Cobos is a Research Scientist and the Director of Research and Development at METER.  He also holds an adjunct appointment in the Department of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics.  Doug’s Masters Degree from Texas A&M and Ph.D. from the University of Minnesota focused on field-scale fluxes of CO2 and mercury, respectively.  Doug was hired at METER to be the Lead Engineer in charge of designing the Thermal and Electrical Conductivity Probe (TECP) that flew to Mars aboard NASA’s 2008 Phoenix Scout Lander.  His current research is centered on instrumentation development for soil and plant sciences.

Case studies, webinars, and articles you’ll love

Receive the latest content on a regular basis.


Good morning, and thanks for joining us for the virtual seminar this morning. The topic we want to talk about today is best practices for measuring thermal properties of fluids with the KD2 Pro and in general with line heat source techniques. So this is the outline of what we want to talk about today. First of all, we’re going to talk briefly about why we might want to measure thermal properties and fluids. And then we’ll talk some about how the KD2 Pro utilizes the transient line heat source technique to make those measurements. And then we want to talk in some detail about the limitations and the practical solutions to get around those limitations for measuring fluids, measuring thermal properties of fluids with the KD2 Pro. We’ll talk in depth about two different types of convection that can compromise the measurements. Then we’ll talk a little bit about good methods for controlling the temperature of samples. And finally, we’ll spend just a couple of minutes talking about some general interest thermal properties on Mars.

So why would we want to measure thermal properties of fluids? Well, there are several different general areas that the thermal properties of fluids become important. First of all, heat transfer fluids, obviously the thermal properties of those really control how well those transfer heat. You could think of antifreeze and internal combustion engines or other cooling fluids is prime examples of this. One other place used in engines that the thermal properties of a fluid are important is actually lubricating oil. So we all think about the oil in your car is being predominantly for lubrication, but it also serves a big purpose in distributing heat throughout that system away from areas with a lot of friction or are with, you know, combustion occurring and moving that heat toward cooler areas. Obviously, if we’re cooking, the oils used in cooking, the thermal properties of those need to be well known so that the cook times and things can be calculated. But the last two bullet points I have up here are probably the ones that are most important to the majority of the people in the audience today. The first of those are thermal grease or heat sink compounds that are used to move heat away from integrated circuits in other components in computers predominantly. And it’s it’s critically important to know the thermal properties of those compounds. But probably the number one reason why most of you guys have tuned in is because you’re trying to measure thermal properties of nanofluids. Nanofluids, of course, are fluids that have nanoparticles generally carbon based, sometimes based on other materials that give those fluids different physical properties from the base fluid. And over the past few decades, there have been some interesting studies that have shown nanofluids to have these amazing heat transfer properties, thermal conductivity that are far beyond what would be predicted by the basic theory. But as it turns out, in the last few years now that people are doing better measurements, making better measurements on the nanofluids, it turns out that a lot of those claims those, outlandish claims were really artifacts of bad measurement technique. And just to show how popular this is, when I first gave this virtual seminar in 2011, I did a Google Scholar search for KD2 Pro and nanofluids and found 91 Peer Reviewed papers. I did that same search a couple of days ago in 2013, and now there are 232 papers that show up. And so there’s quite a bit of interest and quite a bit of research currently being done in those areas. And I think that the KD2 Pro and the in the line heat source techniques have really become kind of the standard to measure the thermal properties or the thermal conductivity of nanofluids. And now the numbers are quite a bit better and actually comparing real numbers to real numbers. So let’s talk about the thermal properties that we care about.

The first one, and the one that we’ll spend most of our time on is the thermal conductivity, which in this presentation we’ll use K to denote thermal conductivity. And this is simply the ability of a material to transfer heat with units of watts per meter per degree k or watts per meter degrees C, those are equivalent. The thermal resistivity of course is just the reciprocal of the thermal conductivity. So one over K is R, one over R is k and so those two can be used. Well, if you know one, of course you know the other.

Other thermal properties that are important are the volumetric heat capacity or volumetric specific heat, which we use C to represent and this is the amount of heat or amount of energy required to raise the temperature of some unit volume or mass by one degree. Also, the thermal diffusivity is the ratio of thermal conductivity to volumetric heat capacity. And this is really a measure of the propagation rate of a thermal disturbance. So, this tells you how quickly heat will move through a substance. Now, I want to make one thing very clear early on and that is that the KD2 Pro will not measure volumetric heat capacity or thermal diffusivity in fluids. Okay, it does a very good job with thermal conductivity in fluids. And it can measure volumetric heat capacity and thermal diffusivity in solids and very viscous things that don’t mix. But at this point, we cannot measure these other thermal properties in fluids. Okay, so now let’s talk a little bit about how the KD2 Pro actually makes its measurements.

And we will eventually get to talking about why it can’t measure those other thermal properties in fluids. So the KD2 Pro uses a transient line heat source technique or a transient heated needle technique. And you can see on the slide a picture of the KS1 needle. It’s a long thin needle, that needle contains a heater that heats the entire length evenly. And the black dot in the middle is a thermistor or a thermometer that measures the temperature of the needle while it’s being heated. And with the KD2 Pro, we actually measure the temperature while it’s being cooled as well. Now, this is a transient technique, meaning that the needle is only heated for a short amount of time. It’s not a steady state technique where the needle is heated until the system comes into a steady state. And you measure the temperature gradient and get your properties that way. That is not a practical method to get thermal properties of fluids. I just read a paper from 1986 where back even as recently as 1986, the thermal properties of water is a function of temperature were not well known. Most of the measurements today were made with steady state techniques, and steady state techniques are quite problematic when working with fluids. And so this paper recommended transient techniques because they typically make better measurements with fluids. So let me show you what we do with this measurement. The needle is heated, as you can see, and the temperature in the center of that needle is measured over time as the needle is heated, which you can see on the graph. This is just a typical plot of time versus temperature. And then the heating is turned off and the temperature decay is measured as well. And so we get a heating curve and a cooling curve with time. And the shape of the heating curve and cooling curve can be well described with this set of equations.

Just a second, there we go. So you’ll see that the change in temperature, well let’s focus on the upper equation, and that’s for the heating curve. So the change in temperature, the temperature rise is equal to the q term, and that’s the amount of heat that’s input into the needle divided by four times pi times the thermal conductivity. Okay, so if we know the temperature rise, and we know a few other things, and we can fit this equation to the heating curve. The cooling curve is really similar. Unfortunately, these are somewhat complex equations. You’ll see the exponential integral in there which is not particularly easy to compute. And so while we do and we have used these base equations that that describe the heat flow and cylindrical coordinates, it’s not always practical to do that.

We have done that in the past but don’t use these per se in the KD2 Pro right now. Fortunately, if you do a series expansion on those, and leave out some of the higher order terms, which it’s been shown quite conclusively in the literature that you can do without introducing significant error, then these equations get quite simple. If you look at the graph that’s shown on the slide, this is the graph of temperature versus the natural log of time. And you can see that that now becomes quite well behaved and becomes a nice linear relationship. And the equation you see on the bottom left of the slide, you’ll see that the thermal conductivity is equal to again, q, that’s the amount of energy per unit length that is input into the needle, divided by four times pi times the slope of that straight line, the slope of the temperature versus log time function that you see on the right in the graph. And so if you use that relationship, we use that relationship on both the heating and the cooling curves, then that gives you a pretty robust measurement of the thermal conductivity of whatever the needles and, in this case, the fluid that it’s immersed in.

You can see in this graph, and it becomes pretty intuitive that the thermal conductivity is proportional to the reciprocal of the slope. So if you look at the blue trace, which is a low thermal conductivity material, you’ll see that the slope of that temperature rise is quite steep. And that of course, is because the heat can’t be conducted away from that needle very quickly. And so you get a big temperature rise. In the yellow trace, where the thermal conductivity is much higher as a value of two, then of course, you get less temperature rise, you get a lower slope. And so it becomes pretty intuitive that you can get the thermal conductivity from that temperature rise. Now, I do want to point out that there can be significant errors in this measurement technique, if the temperature of the sample is drifting while you make the measurement, if the ambient temperature is drifting, and we get around that, for the most part, by doing two things. First, we measure both the heating and the cooling curve. And so that temperature drift tends to drop out if you look at heating and cooling. And we also put a term in the analysis that explicitly solves for linear temperature drift. So while we do a pretty good job of getting rid of the effects of temperature drift, it is still pretty important to keep the sample at a relatively stable temperature during the measurement. Luckily, the measurement that we’re talking about is only 60 seconds in duration. So that’s not as hard as it would be if this were, for instance, a steady state measurement.

So here are the data that you might get out of the KD2 Pro: measurement time, sensor type, and then the measurements that you care about, the thermal conductivity, it’s already calculated for you, okay, the equations have already been worked out in the instrument, and so you get your thermal conductivity value, you get your thermal resistivity value, and then you get an error term. And this error term describes how well the equations fit, or the experimental data that you collected fit the equations that we try and fit those two. And so that’s a pretty powerful diagnostic. If that error term is over about .01, then you can be suspicious that maybe something was awry in the measurement. And we’ll talk a little bit later about how convection in fluids can compromise a measurement. And that becomes pretty apparent in that error term. Also note that if you wish, you can get the full time versus temperature curve from the KD2 Pro. If you want to download all the data, you get all the data and you can do whatever analysis on those data that you want to, but I would think that 99 to 100% of the people who use the KD2 Pro will simply use the processed data that come out because the analysis that we do on the data I think is a lot better than you could practically do without a whole lot of work. Okay, so now let’s talk more specifically about measuring the thermal conductivity of fluids and some of the limitations and practical solutions.

So, when you’re measuring thermal conductivity, as the name sounds or as the name indicates, you really want to measure the conduction which is heat transfer through molecular interaction or heat diffusion through the fluid. There is no bulk fluid flow or bulk fluid movement with conduction. There is another heat transfer term that comes into play in liquids and that’s convection. And this is the heat transfer through bulk fluid flow. If the fluid is moving, it is carrying heat along with it. In fluids, this is a highly efficient mode of energy transfer and can be much much larger than the conductive term. We must get rid of the convective term, we have to have still fluid and measure only the conductive term to get an accurate measurement of the thermal conductivity. So, most of what we’re going to talk about for the rest of the presentation is how to make sure we don’t get convection in the measurement.

Okay, sorry, there we go. So there are two types of convection that we have to deal with. The first is forced convection, and forced convection is bulk fluid flow that’s driven by an external source. And so you could think of this as wind blowing with a pressure gradient or water flowing down a hill. Those are both examples of forced convection. In the context of the measurement that you might make with a KD2 Pro, you have to be careful that the sample isn’t mixing, and that the needle is still relative to the sample. And the first one, the sample mixing, it might, of course, you would say, of course, you wouldn’t want the sample mixing, but we actually have had customers call up and say, hey, we’re getting nonsensical numbers. And in our measurements, you know, we’ve got the water flowing right over the needle. Why is that? I mean, it’s in good thermal contact, why aren’t we getting a good measurement? Like, okay, water has to be still. So you have to have your fluid still, and you have to have your needle still in the fluid. Really, the way that this manifests itself, most often is through vibrations. Okay, this is something that’s not terribly intuitive. But if you have vibrations in the environment where you’re making the measurement, then those vibrations are causing forced convection and bulk fluid movement and compromising your measurement. The vibrations we might see in a lab setting, I’ve put some of those up there. I’m sure there are more but these are ones that we have experience with. And the first and most important one in my mind, is the effect of your HVAC system, your air conditioning or heating system. In a lot of industrial and commercial settings, when the HVAC kicks on, you can hear it. You can feel the vibrations, if you can hear and feel the vibrations, then you can be darn well sure that the KD2 Pro can as well. And so you really want to make these measurements without the vibrations from an HVAC system. Also the cooling fans from your computer or other lab instruments, those produce enough vibrations to compromise the measurements, human activity in the lab, a foot falls, somebody bumping your lab bench, that’s going to mess up the measurements as well. And finally, other general lab equipment. We have a vacuum pump that cycles on and off in our lab that will compromise the measurements. I have a VSA instrument that measures moisture sorption isotherms that sits in my office, and it uses a pump to move air around. That’s enough to compromise the measurements. So how do we get around these vibrations? Well, you can do a few things.

A lot of you that are in physics departments working on nanofluids may have access to an optical table, very large, massive table with some vibration dampeners on it. And that’s a pretty good way to get around the vibrations. Another strategy is to turn off the HVAC system and all your instruments and kick everyone out of the lab. Okay. And that’s a great strategy except it doesn’t do a lot of great things for your lab’s productivity. So my recommendation and this is something that I stumbled on kind of by accident is to make the measurements at night. So the way that I stumbled upon this was I was doing some testing during the development of the KD2 Pro and we thought from the theory that we ought to be able to make good measurements of the thermal conductivity of water, but I was really struggling with that measurement and getting noisy data, data that didn’t make any sense. And just by chance one evening, I left the KD2 Pro on in continuous mode while measuring water and left it on when I went home. And you can see the data that resulted on the graph above. During the day when the measurements were being taken, and there were people in the lab and the HVAC system was on you can see early in that graph that the data are just junk. But then at about six o’clock when the HVAC system turns off in our building and everybody had gone home you can see that the measurements went down perfectly to the book value of .6 and stayed there with almost no noise. When people came in in the morning again and the HVAC system turned back on, the measurements went back to junk. So sometimes this is a nice technique, you can turn your lab instruments off, you can turn your HVAC system off, you don’t really lose any productivity in the lab, because people are hopefully at home anyway. So that’s my recommendation for making these measurements without having to buy any special instrumentation or lose any lab productivity.

Okay, so that’s all we’re gonna say about forced convection. Now let’s talk about the other type of convection. And one that’s a little bit less intuitive, but just as important in the context of measuring thermal properties of fluids. This one is free convection or natural convection. Free convection is the bulk fluid flow that is due to temperature gradients. If you create a temperature gradient in a fluid, you create a density gradient in that fluid. And if you are making, or if that fluid is in a gravitational field, which would be of course on earth, then those density gradients will tend to cause the fluid to mix spontaneously. And so you could think of this, you know, more intuitively, of the old saying that hot air rises. Well, why does it rise? Because it’s warmer, it’s less dense, gravity doesn’t pull on it quite as much. And so it rises in response to that density gradient, and the same thing will happen in fluid. So why is this happening? Why is this important when we’re talking about measurements with the KD2 Pro? Well, for two reasons, one we’ll talk about later and that’s sample temperature control. But the one that’s most important is the fact that the KD2 Pro applies heat to a needle to make the measurement. And so by the very nature of that measurement, you’re creating thermal gradients in the sample. And if you’re not careful, you can get free convection, which is just as harmful as forced convection. It will definitely compromise thermal properties measurements. So let’s talk a little bit about how we can get rid of or prevent the free convection. And let’s do this in a kind of pseudo quantitative way, the equation that I have on the slide now describes the heat conductance from free convection. So that g sub H term is the heat conductance from free convection. And so you can think of that as being the error, okay, you want that to be zero, so we want to minimize that g sub H term. And we’ll talk in the next few slides about some of the factors that we can control that we can use to minimize that free convection error. And the three that we’re going to talk about are in red below. And those are the kinematic viscosity, the temperature difference between the bulk fluid and the needle itself, and also the characteristic dimension of the object or the needle that’s placed in that fluid.

So let’s get started by talking about the fluid viscosity. And I have it circled in red, it’s in the denominator. So we know that as viscosity goes up, the error goes down. And you can see that in the graph, that high viscosity fluids like molasses, or glycerol, or a motor oil have a relatively small error. But as you get less and less viscosity, for instance up to water and things that are less viscous or thinner than water, then you start to get into problems. And you’ll notice that I put water on there twice. One is the viscosity of water at 20 degrees. One is the viscosity of water at 50 degrees. Why did I pick out 50 degrees? Well, experimentally, and I guess, empirically speaking, we and many of our customers who use the KD2 Pro and water have found that that’s about the cut off for as high of a temperature as you can successfully measure water. And the reason is that the viscosity of water decreases with temperature. And so as it gets less and less viscous at about 50 degrees, then even with all the other precautions that we’ll talk about, that’s about as low of a viscosity as you can successfully measure the thermal conductivity. Here’s a graph that’s from our friend Sam Sprint at Kent State University. It’s a little bit busy, but bear with me while I point out some important things. We’re going to be looking at the Y axis on the right hand side, the red y axis, and what this shows is the absolute difference in measured thermal conductivity versus the table values or the known values of thermal conductivity for some different fluids. And notice that the x axis is one over viscosity. So the highly viscous materials are over on the left side. And obviously, the error goes up as you move right and go toward the less viscous materials. And so this kind of gives you an idea of the absolute magnitude of error that you can expect at different viscosity levels. And I’m sorry, these units of viscosity are a little bit different than the meters squared per second that I chose. But those are easy conversions to make.

Some more comments on free convection. First, anything that’s more viscous than water is fine. You should be fine, you should have no problems measuring its thermal conductivity. Water and aqueous solutions are fine up to about 50 degrees C, and that’s a kinematic viscosity of about 5.5 times 10 to the negative seven meter squared per second. So anything, excuse me, that’s more viscous than that should be fine. Anything less viscous than that you may struggle. Now some of our very creative and very good power users of KD2 Pro and fluids can go a little bit above that and measure some fluids that have lower viscosities. But it’s problematic, it gets really difficult to do. So that gives you kind of a practical, lower limit to viscosity. Now another point that I’d like to make about viscosity is that in some fluids, it’s possible to actually stabilize that fluid and increase the viscosity without changing the thermal properties. For instance, if you’re working in water or aqueous solutions, you can stabilize those in any number of ways with starch. With there’s other methods, but the one that we use is agar powder. We actually generally use five grams of agar powder to one liter of water mixture. So about .5% by mass mixture, and that solidifies the water to jello type consistency, it turns it into a solid really. So this prevents any forced convection, it prevents any free convection, and actually allows you to measure all the thermal properties. You can use that dual needle sensor, the SH1 to measure heat capacity, diffusivity, and thermal conductivity. Now, one thing I don’t know, because I’m not a nano-particle guy is if that stabilization process would interfere with the function of the nanoparticles. My guess would be that it would not, but I couldn’t be sure of that, and so you guys who work in nano fluids would be better equipped to answer that question. But just know that this is an option, aqueous solutions may be an option for other solutions as well.

Okay, moving on from viscosity, let’s look at some of the other factors that affect the free convection. And another one of those is the delta T or the temperature difference between the sensor and the bulk fluid. And so since that’s in the numerator, the more sensor heating that we get, the more error we get from free convection. So we want to minimize the sensor heating. This is something that you can’t control. But this is something that we can control. And we have taken into account. And so the KS-1 sensor for the KD2 Pro, the small single needle sensor is specifically designed for fluid measurements. And we put a very small amount of heat into that but make a very precise temperature measurement. To be able to resolve the temperature signal, we need to be able to measure temperatures well within smaller than a 1,000th of a degree C and we can do that. And so we can put just a little bit of heat into the sensor. Now there’s a star up there on the graph, or excuse me on the on the slide. Note that you need to use the low power mode and the one minute read time. So this minimizes the amount of heat that’s put into the sample. This is the way the KD2 Pro comes configured, you would have to go through some special steps to configure it differently. So but just make sure that you’re using low power mode and the one minute read time and you will get very small sensor heating. This is a graph of temperature versus time on a material with a thermal conductivity of about one watt per meter per k, and you can see that we get about .15 degrees heating in that sample. If we were measuring glycerol, the same graph that looks like this, that you saw before, that glycerol has a thermal conductivity of about .285, you get about half a degree of heating in that. So the amount of heating is inversely proportional to the thermal conductivity, of course, but this gives you an idea of how small of a temperature rise you can expect to see in various fluids. And so by minimizing this, we have really minimized the chances of the onset of free convection.

Now, a couple of notes, the measurements that I’ve been talking about, and the temperature levels that I’ve been talking about are valid for the KS-1 sensor, the small single needle sensor that’s developed specifically for fluids. You can see a little table up here that comes from the manual of the KD2 Pro, and it shows you which sensors you can and cannot use in various fluids. So looking at the viscous fluids like glycerol or castor oil or motor oil, KS-1 is best, that you can use the TR-1, the larger single needle sensor in those fluids. But I don’t know why you would, because the KT or excuse me, the KS-1 will be quite a bit more accurate. And there’s really no reason to use the TR-1. TR-1 was designed specifically for soil applications where you have problems with contact resistance between the soil in the needle. You don’t have those contact resistance problems in fluids, it’s one of the beautiful things about working in fluids. So use the KS-1. Low viscosity fluids, KS-1 is the only choice. Now, of course, stabilized water, if you want to measure all three thermal properties and stabilized water, then you can do that with that SH-1 sensor, which is a sensor on the far right, the dual needle sensor. The SH-1, unfortunately, we have to put a lot of heat into that sensor, because we make a lot, well we add heat at one of the needles and make a temperature measurement at the other needle. So we need to make sure we get enough heat across that gap to be able to measure a temperature rise. And so that one has to put in a lot of heat, which is why you cannot measure fluids per se with the SH-1. Now, let me say that if you are one of the fellows working in thermal pastes, or thermal greases, then those typically don’t have the convection problems. And so you can measure all three thermal properties: the volumetric heat capacity, thermal diffusivity, and thermal conductivity with that SH-1 sensor so, but if you’re working in true fluids, then it’s not a good idea, not going to get good results.

So now let’s look at the last factor that affects free convection that we can control. And you can see the d is circled in a couple of places in that equation. That d is the characteristic dimension of the sensor, which is defined by the shape of the sensor and the direction of fluid flow pass that sensor. In the picture you see where the needle is oriented horizontally, if you heat that needle, and the fluid around that needle becomes less dense and wants to rise, then the fluid flow is across the axis of that needle. In that case, the characteristic dimension is very small. It’s just the diameter of the needle or .127 centimeters. If you orient that needle vertically, as in the other picture, then the fluid flow is parallel to the axis of the sensor. And the characteristic dimension in that case becomes the length of the sensor which is six centimeters. Okay, so let’s look back at our equation, that d cubed term is to the quarter power. So on the in the numerator we have d to the three quarters power. And then in the denominator we have d to the one power. So you can see that overall we have d to the negative one quarter power. So we want the largest possible characteristic dimension which will then minimize the free convection. And so we want to use that vertical sensor orientation that will decrease the onset and amount of free convection. So let’s go back to this graph from from Sam Sprunt. This illustrates this pretty well. The y axis on the right hand side is for a vertical needle orientation. The y axis on the left hand side is for a horizontal needle orientation. Notice that the absolute error for the horizontal axis is five times the absolute error for the vertical axis. And that that blue plot is a little higher than the red plot. And so it turns out that you get maybe seven times as much error from free convection if you orient the needle horizontally as opposed to orienting it vertically. So that’s a long winded way of saying, orient your needle vertically, and you’ll have a lot less problems with free convection.

Okay, so those are the things that we wanted to talk about for both forced and free convection. Now, I want to switch gears just a little bit and talk about sample temperature control. The reason I want to talk about this is that we get questions all the time. We get quite a few people who want to measure thermal properties of fluids at some temperature or at over a range of temperatures to characterize the thermal conductivity versus temperature relationship. When you’re doing temperature control, controlling the temperature, you have to take into account the exact same things we’ve been talking about forced convection in the form of vibrations, maybe vibrations, or waterbath currents, and also free convection. If you’re heating something but you’re not heating it uniformly, you’re creating temperature gradients and getting yourself into trouble with free convection. Some examples of forced convection, if you have an oven that you’re using to control the temperature of your sample and that oven has a fan in it, then you have to turn that fan off. You have to turn the oven off before you make the measurement or the vibrations from the fan are going to ruin your measurement. Same goes for cooling with a refrigerator that has a compressor, the vibrations from that compressor will compromise your measurement. And finally, if you’re doing your heating or cooling in a water bath, and you have a circulating water bath, which most of them are, you need to turn that circulation off before you make the measurement.

In here, you can see a couple of examples of free convection that occurs with non uniform heating. Okay, if you’re putting your sample on a hot plate and heating the bottom, that is a horrible idea, you’re heating the fluid at the bottom. It’s becoming less dense, those eddies are breaking loose, floating to the top being replaced by cooler fluid that’s coming down. And so you’re just getting this circulation and this free convection, which will compromise your measurement. We’ve had quite a few people try this and they all have very bad measurements. So don’t do that.

Okay, now that I’ve told you what not to do, let me give you a couple of ideas on what you can do to get good temperature control. Two separate methods, this one is my personal favorite. And with this method, you would bring the sample and needle and everything into equilibrium at the desired temperature in a water bath. Then you turn off the water bath, wait for the circulation to stop and make your measurements before the temperature begins to drift or change. This has a couple of advantages in the water bath. One, the water bath has a really big thermal mass, so you can turn the water bath off and the temperature is going to change very, very slowly in that water bath. So you shouldn’t have any problems with temperature drift while you make the measurements. Also, the large mass of that water tends to dampen vibrations, okay, so you might be able to get away with making measurements in the water bath, even during the day with HVAC on, just depends more on how much that shakes your building and what lab equipment you’ve got going on that may be interfering as well. But that gives you a nice buffer to vibrations and a nice buffer to temperature drift. So this is a pretty good method. Of course, if you have to go up over 100 degrees C with your measurements, then that doesn’t work so well. You can also make your measurements in an oven. Okay, so bring your sample and the needle to the desired temperature in the oven. And then if you happen to have a circulating oven, turn the oven off to get rid of the vibrations and make the measurements before the temperature begins to drift. Now in an oven when you turn the especially if you’re at a high temperature when you turn the oven off, then you can get pretty rapid temperature drift back toward ambient. I recommend using a thermal buffer and you can see in the picture on the slide that we have machined a couple of holes to hold our sample vials in a big aluminum block, a big thermally massive aluminum block that does two things. One, the thermal conductivity of aluminum is very high. So that tends to be an isothermal system so you don’t get temperature gradients and free convection, and also gives you a big thermal mass so the temperature doesn’t drift as quickly as it would once you turn your oven off. You can see in the data on the right, these are some data that we collected on customer samples, customers actually send us samples and we provide a service to characterize the thermal properties. You can see that we’ve made some really nice measurements on this is just a random set that I grabbed on a glycerol water mix that made measurements up to about 90 degrees C. At that point, the kinematic viscosity got too low. And the measurements got pretty poor, but we were able to characterize up to about 90 degrees C. So this is a method that works pretty well.

So those were the main things that I wanted to talk about today. I want to make sure you go home with the important points, just want to reiterate those one more time. First, no convection, you have to prevent convection, you have to prevent forced convection, get rid of your vibrations, maybe make your measurements at night, maybe an optical table, maybe do them in a water bath, gotta get rid of free convection, too. The higher the viscosity, the better. It’s best to stabilize the fluid, if you can, you may not be able to do that. But higher viscosity is always better. Less heat is better. Okay, less needle heating. We’ve taken care of that with the KS-1 sensor. So make sure you use the KS-1, use it in low power mode, use it with one minute read time. And finally, orient the needle vertically. Horizontal orientation will give you problems. Orient it vertically.

Okay, so now that we’ve talked about the important things, I just wanted to spend a couple of minutes, this is a relatively short presentation. So I thought it could spend a couple of minutes talking about some just fun, general interest things that we did here at Decagon. A few years ago, NASA’s Jet Propulsion Lab approached Decagon and said, Hey, can you guys make a sensor to fly to Mars? And we originally said no. But eventually we said yes, we’ll do that. And so that’s how the thermal and electrical conductivity probe came about. And it’s interesting here, I think, because we made the exact same thermal properties measurements that we’re talking about today on the surface of Mars. And the picture you see on the right up there was taken on the surface of Mars and that silver instrument that you see with, of course, some heated needles on it, that’s the thermal and electrical conductivity probe that we made.

So the purpose of the instrument was to measure the thermal and electrical properties of the Martian soil. And the electrical properties we measured were dielectric permittivity, and electrical conductivity, which tell a lot about the presence of liquid water, which was not found. But we also measured the thermal properties, which were useful for a lot of things but inferring liquid water content is one, also inferring ice content is another, and finally getting some interesting information about the pore size distribution and the density of that soil material. You can see in the lower picture there, that actually is another picture from Mars with the thermal and electrical conductivity probe, making measurements in the Martian soil regolith. So what did we find out? Well, we did a complete thermal characterization of the soil or regolith, around the landing site. And those results did a pretty good job of validating some satellite derived data. There are several satellites that orbit Mars and measure surface temperatures and if you know the solar loading, and you can measure the surface temperatures, you can get an idea of the thermal properties. And we validated those measurements for the landing site. And also the measurements indicated no surprise, a low density and dry regolith material with thermal conductivity values that were quite a bit less than what you would see on Earth. That’s because of the low atmospheric pressure and the specific heat values that indicate very dry and very low density soil. So that was just a little bit of fun. It was something that was pretty exciting because that was my first project when I got here to Decagon but that’s all we have for the presentation. I’ll be happy to entertain any questions. Use the question function on your screen there and send in some questions and I’ll answer as many of those as I can in real time. Whatever we don’t get to we will answer via email later on. Thanks a lot.

icon-angle icon-bars icon-times