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# Engage Your Brain (Page 2)

Posted by Jan on 22 March 2020

TL;DR: Pololu is hurting. Skeleton crew is shipping important products to customers fighting COVID-19 around the world. Most employees facing layoffs. Please donate to help us keep operating and spread the word. Donate here.

Please see new update posted Sunday, 29 March 2020.

The last time I posted on this blog was in November 2018, sixteen months ago. We have been very busy at Pololu since then, and there is so much good and positive to share about what we did in 2019 and so far in 2020. Unfortunately, what is compelling me to post this update and plea for help is the COVID-19 coronavirus pandemic that is engulfing our planet.

Nevada, where we are located, was one of the earlier states to mandate sweeping reductions in activity, with the governor announcing on the night of Tuesday, March 17, that all nonessential businesses statewide were to shut down starting on Wednesday. We immediately began contacting all of our employees on Tuesday night not to come in the next morning while trying to plan for a shutdown of operations and looking for clarity about the extent to which the order applied to a company like Pololu. On Wednesday through Friday, we operated with a skeleton crew of around twenty of us, making sure we got orders out and received important incoming shipments.

Pololu is probably not the kind of company that first comes to mind when you think of Las Vegas. We design, make, and sell thousands of products from our location just a few miles from the famous Strip. Here is our building when I was locking up Friday night, with the light from the Luxor pyramid lighting up the clouds:

Inside, we have millions of dollars of equipment on which we have made millions of electronics boards that we have shipped all over the world. This is the newest of our three electronics production lines that we just finished setting up earlier this year, with machines that were all installed in 2018 and 2019:

We have around two dozen pieces of big equipment, each one of which was a substantial undertaking just to install, with the electrical work alone costing tens of thousands of dollars in the most demanding cases. Here is the delivery of our latest laser cutter in November:

We finally completed installation last month, and here is a picture I took for fun three weeks ago, with our first laser cutter from 2003 inside the new one:

I post these pictures to help illustrate that an operation like ours takes a huge amount of effort to build up. I’ve been working on it for twenty years, and there are about 75 more people working on it with me now. We make and ship physical things, so we can’t just do this over a remote computer connection.

And what do we make? For the most part, we make components, like motor controllers and sensors, that go into bigger systems. We do not know most of the applications our products go into, and we cannot disclose some of the more intriguing ones that we do know about. But we have specific confirmation that our products are being used around the world in this fight against the new coronavirus, from components in prototypes for ventilators to components in PCR equipment, including ones used for coronavirus testing.

Are we essential, or essential enough to keep operating? With the changes we had already implemented prior to the governor’s order (for example, we suspended order pick-ups, so we are not open to the public), it seems clear that we are legally allowed to operate in accord with the clarifications the government has been issuing since Friday. Even before the ordered closures, we had done things like stagger our production work benches and spread out the tables in the break room:

(FYI several groups of our employees live and carpool together, so a few chairs at one table seemed ok to have as an option. We’re probably going to spread out chairs more and go to one assigned table per employee tomorrow.)

We are scrambling to stay in operation and to do it as safely and ethically as possible. The strain of trying to run this size of operation with two dozen people is enormous, especially while constantly having to prepare for being shut down externally and dealing with more and more uncertainties about components we need arriving. We are trying to get more people set up to work remotely, but I am posting these pictures to try to show how we cannot just run with remote workers. We are prioritizing shipping what was already made and making priority products that we know are for especially important customers.

I don’t know how much longer we will be able to run. Maybe a few more days? Perhaps even fewer of us can get some especially critical orders out for longer, as long as the shipping companies keep shipping our packages. Things keep changing, so it’s hard to say. But if we are not shipping orders, or just a small fraction of the usual ones, money won’t be coming in. I have told my employees that they should not count on another paycheck beyond the one they just got on Friday. We will keep paying for health insurance for everyone as long as we can, and we are looking into the ramifications that would have for things like unemployment insurance.

There is no way we can survive a shutdown of many weeks or even months. We tried to have redundancies in our operations, with as many backups as reasonable. We even have two compressors that we alternate each week, so that all our machines are not crippled if one of the compressors has an issue:

But we just are not prepared for the level of shutdown we are facing. And so here I am, writing this post on Sunday night after coming up with a plan for tomorrow for my employees and writing to my landlord for help, begging anyone who reads this to help. I know many people and small businesses everywhere are hurting now. I am sure everyone who has put their life into their business feels like theirs is special. We have the facility and the machines and the people who know how to churn out millions of units of hundreds of designs that people around the world use. We have an awesome team. We just need the money to survive until we can start running again.

We are of course looking everywhere for help, and will be applying for whatever disaster relief is available as it becomes available. This is an unprecedented time for us, as I know it is for everyone. I know it is so much to ask for money with no strings attached, especially at a time like this, but that is what will most let us pay our obligations and our employees without diverting resources into extra accounting and agreements. We have set up an emergency product on our website that will allow anyone to donate money to Pololu.

# New products: 1- and 31-channel QTR HD reflectance sensor arrays

Posted by Jan on 31 August 2018

This week, we released what we expect to be the extremes of our new line of QTR HD reflectance sensor arrays, with two sizes of a single-sensor board on the small end and a massive 31-sensor array for the maximum size. This picture shows the relative sizes of the boards, along with some of the intermediate sizes we have available:

We made the two single-sensor sizes because we could make good arguments for each one. Part of the point of doing a single-sensor board is to make it really small, so you can fit it into tight spaces. But “really small” means different things depending on the dimensions you care about. So we have one version that is only 5 mm (0.2") wide, with components on both sides of the PCB, and one version that is 7.5 mm (0.3") wide, with components on just one side. The 7.5 mm wide version is a little thinner and flatter because it doesn’t have parts on one side, can be used with a 3×1, single row connector, and costs slightly less because of the single-sided assembly.

As I mentioned in some of my earlier posts (here and here) about this new line of sensor arrays, we are using two sensor types: more economical units we are calling “QTR”, and higher-performance units with lenses that we are calling “QTRX”. The main appeal of the QTRX sensors is that they can give the same readings at much lower IR emitter currents, which can really make a big difference for big sensor arrays. But if you crank up the current in those QTRX sensors, you can also get more distance. We did not do that on the QTRX arrays because the sensor modules leak light out the sides and interfere with each other when they are closely spaced, but with these single-channel boards, we are also making available the QTRX sensors with the higher 30 mA maximum emitter current, which allows for a range of up to about 8 cm (about 3 inches). We are calling these sensors QTRXL.

This video (taken with an old camera that does not have as much IR filtering as most newer cameras) shows the IR light leakage around the side of the QTRX sensor module:

I should point out that all of these new QTR modules offer variable brightness control by varying the current through the emitter using the control pin. However, if you want to take advantage of the maximum brightness and range, and have several sensors close to each other, you will need some barriers between them to prevent them from blinding each other (or just turn on one emitter at a time).

The 31-sensor arrays are huge! Well, at least compared to the tiny single-sensor boards.

The routing on those boards is quite complex because adjacent IR emitters are not just wired in series (because we want to have separate even/odd emitter control, plus the alternate density population options I discussed in this post), so we ended up having to go to a 4-layer PCB to route it. This did let us make the vertical dimension a little lower, so the board is just 16.5 mm tall, compared to the 20 mm board height for the versions with 15 and fewer sensors. The 31-channel board is also 0.062" (1.6 mm) thick, compared to the thinner 0.040" (1 mm) boards we use for the lower channel counts. You can compare all the dimensions of the various boards in the detailed dimension diagram (1MB pdf).

The sixteen new boards we released this week brings the total available in this new QTR HD product line to 40. You can see the options neatly summarized in the tables below to pick the best array for your application.

QTR sensors
2.9 V to 5.5 V; 30 mA max LED current(1); 5 mm optimal range
Board
width
Configuration Max board
current(2)
Max range Output
type
Name 1-piece
price
5.0 mm 1 sensor (HD)
32 mA 30 mm analog QTR-HD-01A $2.33 RC (digital) QTR-HD-01RC 7.5 mm 1 sensor (MD) 32 mA 30 mm analog QTR-MD-01A$2.15
RC (digital) QTR-MD-01RC
10.2 mm 4 mm × 2
32 mA 30 mm analog QTR-HD-02A $2.74 RC (digital) QTR-HD-02RC 17.0 mm 4 mm × 4 62 mA 40 mm analog QTR-HD-04A$4.04
RC (digital) QTR-HD-04RC
29.0 mm 8 mm × 4
62 mA 40 mm analog QTR-MD-04A $4.22 RC (digital) QTR-MD-04RC 4 mm × 7 125 mA 40 mm analog QTR-HD-07A$6.88
RC (digital) QTR-HD-07RC
61.0 mm 8 mm × 8
125 mA 40 mm analog QTR-MD-08A $7.95 RC (digital) QTR-MD-08RC 4 mm × 15 250 mA 50 mm analog QTR-HD-15A$13.86
RC (digital) QTR-HD-15RC
125.0 mm 4 mm × 31
495 mA 50 mm analog QTR-HD-31A $27.82 RC (digital) QTR-HD-31RC QTRX sensors 2.9 V to 5.5 V; 3.5 mA max LED current(1); 10 mm optimal range Board width Configuration Max board current(2) Max range Output type Name 1-piece price 5.0 mm 1 sensor (HD) 5 mA 30 mm analog QTRX-HD-01A$3.07
RC (digital) QTRX-HD-01RC
7.5 mm 1 sensor (MD)
5 mA 30 mm analog QTRX-MD-01A $2.89 RC (digital) QTRX-MD-01RC 10.2 mm 4 mm × 2 5 mA 30 mm analog QTRX-HD-02A$4.22
RC (digital) QTRX-HD-02RC
17.0 mm 4 mm × 4
9 mA 40 mm analog QTRX-HD-04A $7.00 RC (digital) QTRX-HD-04RC 29.0 mm 8 mm × 4 9 mA 40 mm analog QTRX-MD-04A$7.18
RC (digital) QTRX-MD-04RC
4 mm × 7
17 mA 40 mm analog QTRX-HD-07A $12.06 RC (digital) QTRX-HD-07RC 61.0 mm 8 mm × 8 17 mA 40 mm analog QTRX-MD-08A$13.87
RC (digital) QTRX-MD-08RC
4 mm × 15
34 mA 50 mm analog QTRX-HD-15A $24.96 RC (digital) QTRX-HD-15RC 125.0 mm 4 mm × 31 68 mA 50 mm analog QTRX-HD-31A$50.76
RC (digital) QTRX-HD-31RC
QTRXL sensors
2.9 V to 5.5 V; 30 mA max LED current(1); 20 mm optimal range
Board
width
Configuration Max board
current(2)
Max range Output
type
Name 1-piece
price
5.0 mm 1 sensor (HD)
32 mA 80 mm analog QTRXL-HD-01A $3.07 RC (digital) QTRXL-HD-01RC 7.5 mm 1 sensor (MD) 32 mA 80 mm analog QTRXL-MD-01A$2.89
RC (digital) QTRXL-MD-01RC
1 Can be dynamically reduced to any of 32 available dimming levels.
2 With all LEDs on at max brightness setting.

Our introductory promotions are still going strong! Be one of the first 100 customers to use coupon code QTRINTRO and get any of these new sensors at half price! (Limit 3 per item per customer.)

# New products: Robot Arm Kit for Romi (and also just the gripper)

Posted by Jan on 30 August 2018

I’m super excited to announce our newest product, the Robot Arm Kit for Romi. The Romi arm is designed to mount to the back half of a Romi chassis with two fixed servos controlling the height and angle of the gripper through a nifty linkage system.

The gripper itself uses a micro servo with two parallel fingers or paddles that open and close through a rack and pinion arrangement. Here is a quick video demonstration of a Romi chassis with the arm attachment:

You can see the available range of motion in the drawings below:

The kit ships with all mechanical parts, including special servos with a fourth wire for reading the position of the output shaft:

We are also making the gripper used on the arm available as a standalone Micro Gripper Kit with Position Feedback Servo. Here is a picture of the assembled gripper:

Products like this arm kit, with many injection-molded components, are some of the most complicated and time-consuming products we make. As those of you who have followed our growth over the past decade are probably aware, we try to develop our more complete robot kits incrementally, starting with components like just a wheel or a motor bracket, and then using those components in the more integrated robots. For example, we came out with this line of wheels in 2010:

The Romi and Balboa robots, which use those wheels, did not come out until 2016 and 2017.

If you look at the parts that go into just the gripper portion, you can see that each of the components is roughly as complicated as one of those wheels, and you can’t really do much with just one of those parts:

So, a lot of work goes into designing these kits. We also do not machine the molds or do the injection molding in-house (we did that on the first few parts for the 3pi robot), so that adds a lot of delays compared to our electronics boards, which we make in the same building that we design them in. We do 3D print prototypes to maximize the chances that we get the designs right, but there are invariably little modifications that we end up having to make when the components are this complicated, which is why it takes us years to go from the initial idea to the released kit.

We are at least sticking to our incremental product release approach as far as integration with electronics goes: at the time of the Romi arm attachment release, we do not have a specific solution for controlling the robot, which we will be working on next. Therefore, this kit is currently intended for advanced users who are comfortable powering and controlling several servos on their own.

As with all of our new product releases this year, we are offering substantial introductory discounts for the first customers to try out our new designs. You can use coupon code ROMIARMINTRO to get the whole arm for just $49 and code GRIPPERINTRO to get just the gripper for only$13. Each coupon is limited to 100 uses and 3 units per customer.

# New products: more new QTR HD sensor arrays by student engineering interns

Posted by Jan on 23 August 2018

All the student engineering interns we had over the summer from out-of-town colleges are headed back to school, so I get to announce the release of products they worked on over the summer. The new QTR sensors we are releasing today include the 15-channel version laid out by seventeen-year-old Chris H.

You can see more about our new line of QTR reflectance sensor arrays in the first blog post I wrote about them a few weeks ago. One cool design and manufacturing aspect I did not mention then is that we designed these boards so that they could be populated at various densities. For example, that lets us make an 8-channel version with 8 mm sensor pitch on the same board that also works as a 15-channel array with 4 mm sensor pitch:

Here are some diagrams showing some of the thought that went into the soon-to-be released 31-channel version, which can also be populated to be an 8 mm pitch, 16-sensor array; a 12 mm pitch, 11-channel array; and a 20 mm pitch, 7-channel array:

With so many combinations of sensor types and output circuits, we won’t make every one of the possible arrangements a stock product, but the idea is that if you have an application where a particular sensor pitch is ideal for you, we can quickly make some for you without having to lay out new PCBs.

We expect eight channels on an 8 mm pitch to be a popular variant, so those will be stock products. We have also added the corresponding 4-channel version (using the same boards used for the full-density, 7-channel product), so this new product announcement covers twelve new stock sensor arrays:

Our introductory promotions are still going strong! Be one of the first 100 customers using coupon code QTRINTRO and snag any of these new sensors at half price! (Limit 3 per item per customer.)

# New product: 80×10mm wheels with multi-hub inserts for 3mm and 4mm shafts

Posted by Jan on 29 June 2018

I have some exciting new wheels to tell you about (available as an 80×10mm black pair and an 80×10mm white pair). With a few small exceptions, all of the wheels we have made so far were for press fits (more properly called interference fit) onto 3mm D shafts such as those on our micro metal gearmotors. The press fit is simple and convenient for smaller motors and wheels, but there is an inherent trade-off between how hard you have to push to get the wheel on the shaft and how well the wheel stays on the shaft. As we contemplated designing some new wheels for our growing lines of 20D gearmotors and 25D gearmotors with 4mm output shafts (and higher power), I wanted something better. Our wheels already worked with our machined hubs with set screws, like this:

But the machined hubs are expensive, more expensive than the rest of the wheel. There’s also the much more minor issue of the machined hub option only allowing for the wheel to be placed at the very end of the shaft unless you drilled out the plastic wheel to have a hole larger than the shaft. I wanted to have an all-plastic, injection moldable solution that involved multiple parts that would somehow clamp the wheel onto an axle, kind of like a chuck on a drill.

My initial idea was to have just two parts: the outer wheel and an inner, interchangeable collet that would get wedged between the wheel and axle. But our mechanical engineers were not able to come up with a single part that could both compress onto the shaft and attach rigidly to the outer wheel. Because the parts are so small, the resolution of our 3D printer limited the effectiveness of prototypes, so we worked with scaled-up models. This picture shows one earlier model next to the final production parts for scale:

The other side of that model shows what we were thinking about for holding nuts in place on the back side of the wheel:

At that point, we were at a three-component design, plus the three screws and nuts, which was turning out to be difficult to assemble onto a shaft, even if it worked. The screw heads needed to be accessible from the outside of the wheel so they could be tightened, and that left the nuts near the motor where they were difficult to access, and trying to make the wheel hold the nuts required the wheel to be toward the motor and the collet piece on the outside, which was less aesthetically appealing.

So, in the end, we gave up on my all-plastic goal and designed a single stamped plate with threaded holes that clamps the wheel onto the collet insert. It definitely makes the assembly much easier, as you can see from this expanded view:

Having a design that seems like it might work on a 3D printed mock-up is still quite different from getting it working on the final, injection-molded parts. The clamping action of the collet inserts might have given us a little more margin for error than our usual press-fit wheels, but on those, a wrong fit is relatively straightforward to adjust: start with the fit a little on the loose side, and if it’s too loose, make the pin (and hole) smaller until it’s tight enough. With the new wheels, there were many more things that could go wrong, including alignment (wobbling). There was also the unknown of how much torque the hubs would take.

In the end, I think we arrived at a nice performance point. The wheels cannot take as much torque as if they were screwed on to the machined hub with set screw, but they can do much more than just the press fit hubs while putting less strain on the motor output shafts during installation. It’s possible to assemble the wheels with a little wobble, but if it’s a concern in your application, you can fiddle with how you tighten the three screws to get it as lined up as you like.

We started with our 80×10mm wheels, and made inserts that work with 3mm and 4mm shafts, both round and D-shaped:

Since the concept seems to be working, we will be working on different wheel sizes and inserts for larger shafts later this year.

As with all our new product introductions this year, we are having an introductory special. Be among the first 100 customers to use coupon code MULTIHUBINTRO (click to add the coupon code to your cart) and get 33% off on up to three sets.

# New product: Stability Conversion Kit for Balboa (and some memories of my first robot)

Posted by Jan on 27 June 2018

I don’t blame you if you have no idea why the new Stability Conversion Kit for Balboa is so exciting. With a name like that, you probably couldn’t even guess what it is, let alone why it’s exciting. But let me keep you guessing while I share a little about the first robot I built, which is kind of a hint. It’s pure coincidence that I happened to get reunited with it just as we were preparing to release this new product I’m announcing here.

I know for sure that I built my first robot in eighth grade, for the science and engineering fair for which everyone in my school had to do a project, which means I must have started working on it in 1992 when I was twelve years old. The better projects in my school went on to the local, island-wide science fair in Hilo, and the better projects there went on to the state fair in Honolulu. (I was initially not among those chosen to go on from the Big Island, I think because of some judging process mistake, but my science teacher and probably others lobbied to get me there.) There was time between the different stages, so I kept working on it through the spring of 1993, which would now make it over 25 years old. I probably added the labels in later stages in response to some advice to better present what I made.

When Paul saw the robot for the first time in my office this morning, he immediately recognized a piece of it: “That looks like the gearbox from my first robot!” I was a bit skeptical, but he immediately backed it up by pulling out Gordon McComb’s Robot Builder’s Bonanza and showing me the project he had followed from the book. (In another amusing twist, it turns out that the copy of the book Paul had in his office is my old book, though I hadn’t gotten it until high school, and I didn’t realize until today that the gearbox in the book was the same one I had used.)

I’m pretty sure I got the gearbox from Edmund Scientific, because their printed catalogs and Radio Shack (nearest one in Kona, 40 miles away) were initially my only sources for anything electronics-related. The wheels were from some broken radio control toy. The ball caster was long a point of frustration. In earlier versions of the robot, I had tried more common swivel casters and then a ball caster I made from a ping pong ball in a toilet paper tube, but neither was very reliable. I was very happy to eventually find the metal ball caster that I used in the final version, which you can see here along with the three IR LED and phototransistor pairs used for detecting a two-inch white line on a black background:

That heavy, noisy caster was not ideal, but at least it didn’t jam at a bad angle like the swivel caster or collapse like my ping pong ball and cardboard creations. I am mentioning all these details because it was so much work just to put a basic chassis together, without even getting to the electronics part. The electronics are not something I want to cover in this blog post, but I should mention that I was very fortunate to find a mentor at the Canada-France-Hawaii Telescope headquarters right across the road from my school. CFHT had a nice electronics lab stocked with all kinds of components they just gave me and tools they let me use, and I got lots of help from John Horne when I was in 8th grade and then from many others there while I was in high school.

So, to bring this back to Pololu’s new product: the Stability Conversion Kit for Balboa is primarily a ball caster attachment for the Balboa chassis:

That might sound pretty basic, but using it fundamentally transforms the Balboa into a very different kind of robot. As a reminder, Balboa is a two-wheeled, balancing robot:

You can read more about the balancing robot in my blog post introducing the Balboa robot. It’s a very capable platform that we spent many years developing, but making a balancing robot is not easy, and it’s probably not the best type of robot to build as your first robot. We did not even release the chassis as a separate product independent of electronics because it would be difficult to do much with it. The new stability conversion kit completely changes that. With the ball caster, the chassis can be used as a much more beginner-friendly differential-drive mobile base with three points of contact with the ground:

We offer the ball caster attachment by itself, for those who want to use it with a complete Balboa 32U4 robot kit to immediately get up and running without developing their own electronics. We also now offer the Balboa Chassis with Stability Conversion Kit, which includes all the mechanical components for the chassis other than wheels and motors:

As with the original Balboa 32U4 kit that includes electronics, we deliberately do not include motors and wheels so that you can pick your own to customize the look and performance of your robot. This diagram shows the possible chassis angles with four different wheel sizes ranging from 60 mm through 90 mm:

For motors, we recommend our 30:1 HPCB, 50:1 HPCB, or 75:1 HPCB micro metal gearmotors with extended back shafts that can be used with encoders. Even if you do not plan on using encoders on your robot at first, it’s nice to have the option down the road.

And options are what our chassis kits are all about, whether you select our Zumo tracked chassis, Romi round chassis, or now the new Balboa chassis. One of my guiding principles in developing our robot platforms is that I want to help you, our customers, build your robot, not just the particular one we designed.

I realize there are many kids interested in robotics who are not as fortunate as I was to have Canada-France-Hawaii Telescope headquarters across the street from my middle school, and that for many of them (and their parents and teachers), all of the options we offer can be overwhelming. Over the next several years, we will be working on sensors and other modules specifically for the Balboa, along with combination bundles and tutorials that will make Balboa a platform that students can begin with as a basic first robot in middle school and keep expanding through higher levels of their education.

I’ll end this product introduction as I have all my product announcements this year, with an introductory special to encourage you to try the Balboa chassis out for yourself. Be among the first 100 customers to use coupon code BALBOACHASSIS (click to add the coupon code to your cart) and get 15% off on Balboa-related products (limit 4 per product).

# Mycronic MY600 solder paste jet printer first print

Posted by Jan on 30 May 2018

Installation of our new Mycronic MY600 that arrived earlier this month is going smoothly. Here are a few pictures and a video from our first test print on a Pololu PCB panel.

That footage of the jet printer in action is not sped up!

# MY600 solder paste jet printer arrived!

Posted by Jan on 18 May 2018

In my February post about our new equipment, I wrote about why I did not get a jet printer for solder paste. Well, I ended up getting one after all, and it arrived today.

We have a great building, but we don’t have loading docks, which always makes these big equipment deliveries a bit more of an adventure. Despite assurances that the crate would be at the back of the truck (and that it would have a lift gate that could handle the weight), it arrived way at the front.

At least it wasn’t as big of a crate in as deep of a truck as this time. The Mycronic MY600 jet printer is not the biggest machine, but it weighs a ton because of its granite base. And by “it weighs a ton”, I mean literally more than two tons. Especially with the weight of the crate and the other accessories in there, it was way too much for the lift gate. We tried to get two pallet trucks under it but could not get it to move, even after repositioning the truck to make the crate moving downhill.

The big forklift we rented had not arrived yet, but our smaller forklift was able to add enough pulling power to get the crate to the back of the truck.

I like noticing that silver Honda in the back of some of these pictures. Here it is almost sixteen years ago (more about our first ten years in Vegas here):

The big forklift arrived just in time to keep us from attempting some small forklift plus lift gate kind of stunt.

The crate had a very lopsided weight distribution, and the crate had some peculiar skids that required some precision alignment to get the fork into the pocket. (The small gap was too small for the pallet trucks, which contributed to the earlier difficulty in moving the crate with pallet trucks.)

I really liked truck driver Sharrieff, with a great “we’re going to work together and we’re going to get this crate down” attitude. I just noticed now as I wrote this up that he’s the owner of his trucking company.

With the crate in our warehouse and the sides removed, it was easier to see why the weight distribution was so off-center.

And here it is in its temporary home next to the Europlacer stencil printer we got earlier this year:

It’s a temporary home since we will be doing some major remodeling of our building later this year. At the moment we have two full SMT assembly lines, with the newest pick-and-place machine separately on its own in a batch setup. Once we free up more space on the main manufacturing floor, we should be putting the jet printer in line with the pick and place machine in a third complete line that should be ultra-optimized for efficient manufacturing in small quantities. Installation of the new jet printer isn’t until after Memorial Day, and I will be sure to post more updates once we have the machine in action.

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# How to back-transform glmer() cbind() proportion data

I have fit a binomial GLMM (glmer) to a response variable that is a proportion.

How my data looks:

ind_id   total_off  total_on  total  OFT1  MIS1  sex  litterid
122      50         40        90     2.33  -1.32  F    5643
134      10         20        30     -1.00 -0.22  M    2345
145      1          10        11     3.19   0.89  F    8743

Where:

• ind_id: individual identity
• total_off: count of # of times off territory
• total_on: count of # of times on territory
• total: sum of # of times off and on territory
• OFT1: continuous behaviour score
• MIS1: continuous behaviour score
• sex: categorical sex (M or F)
• litterid: identity of litter

Model:

library(lme4)
m.1<-glmer(cbind(total_off, total_on) ~ OFT1 + MIS1 + sex + (1|litterid), family=binomial, data=new)
summary(m.1)

Fixed effects output:

            Estimate Std. Error z value Pr(>|z|)
(Intercept)  0.08019    0.19200   0.418    0.676
OFT1        -0.07030    0.06919  -1.016    0.310
MIS1         0.05392    0.04753   1.135    0.257
sexM         0.19664    0.19385   1.014    0.310

Based on chapter 16 in The R Book, I can back-transform the estimates with the following equation:

1/(1+1/(exp(-0.07030)))

But, the examples Crawley goes over are for categorical explanatory variables with a glm() model (no random effect), not glmer() model (random effect).

I have two questions:

1. For starters, should my cbind() actually be cbind(total_off, total) instead of cbind(total_off, total_on)?
2. How can I back-transform glmer model estimates? Once back transformed, how are they interpreted?

For continuous explanatory variables, such as OFT1, I want to be able to say something like: for every 1 unit increase in OFT1 score, an individual spends X% more/less time off their territory. For categorical explanatory variables, such as sex, I want to be able to say: females spend X% more/less time off their natal territory compared to males.

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Events
Spring 2019

# Bethe approximation is an optimization-based framework for approximating partition functions, with roots in statistical physics. *starts at 10:30 a.m. sharp*

Mar 13, 2019 10:30 am – 12:00 pm

Parent Program:
Speaker:

Nisheeth Vishnoi (Yale University)

Location:

Room 116

While it is known that the stationary points of the Bethe approximation coincide with the fixed points of belief propagation, the relation between the Bethe approximation and the partition function is not well understood. It has been shown that for a few classes of factor graphs, e.g. permanents and attractive graphical models, the Bethe approximation always gives a lower bound to the partition function, which distinguishes them from the general case, where neither a lower bound, nor an upper bound holds universally.

We consider bipartite normal factor graphs and show that if the local constraints satisfy a certain "analytic" property, the Bethe approximation is a lower bound to the partition function. We arrive at this result by viewing factor graphs through the lens of polynomials. In this process, we reformulate the Bethe approximation as a capacity-like polynomial optimization problem. Our sufficient condition for the lower bound property to hold is inspired by recent developments in the theory of real stable polynomials.

Based on a joint work with Damian Straszak available at https://ieeexplore.ieee.org/document/8653402


text:
# Why does smoke rise?

• I
• jaumzaum
jaumzaum
If we consider that the heating of the products created in the combustion of paper to be an almost adiabatic process (as it's extremely fast), then
$$T^{1.4} P^{-0.4} = const.$$
If we double the temperature, we multiply the pressure by 11.3
The density of an ideal gas is
$$\rho=PM/RT$$
If we double the temperature, the density would be multiplied by 5.7

So why would the smoke rises if it became more dense?

Mentor
If we double the temperature, we multiply the pressure by 11.3
How can that be if the combustion isn't happening in an enclosure?

Dale and etotheipi


text:
# Three equations with square variables

Let

a^2+b^2+ab=9

b^2+c^2+bc=16

a^2+c^2+ac=25

What is the value of ab + bc +ac?

Note by Sinuhé Ancelmo
4 years, 6 months ago

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Take a point $$O$$ from which three line segments $$OA, OB, OC$$, lying on the same plane, emanate such that their respective lengths are $$a, b, c$$ and the angle between any two of them is 120 degrees. Use Cosine rule and the mentioned equations to find $$AB, BC, CA$$ which will have values 3,4 and 5 units. Now this triangle $$ABC$$ is right angled( by the converse of Pythagoras' Theorem), so it's area will be 6 sq. units. Also, the area of the three triangles $$AOB, BOC, COA$$ can be found out by the sine rule (individually), and now equate it with the earlier found area. The answer comes out to be $$8 \sqrt{3}$$. [ the calculations are to be done by you].

- 4 years, 6 months ago

Is it possible to solve this algebraically?

- 4 years, 6 months ago

Well, your idea is interesting, but are you sure about the value of ab + bc +ac? shouldn' it be 8*sqrt(3)? Could you chek it again, plz??

- 4 years, 6 months ago

Could you show any algebraic, only algebraic solution, friend?

- 4 years, 6 months ago

Edited.

- 4 years, 6 months ago

You can realize that - 8 * sqrt (3) is also a valid answer for ab + bc + ac, right?

- 4 years, 6 months ago

- 4 years, 6 months ago

what will be the algebric solution Help me

- 2 years, 10 months ago

The solution could be algebraical or geometrical, Thanks in advance for help!

- 4 years, 6 months ago


text:
## Introduction

Cardiopulmonary bypass (CPB) is a standard procedure in cardiac surgery1,2. The distal ascending aorta is primarily chosen for cannulation, positioning the cannula close to the origin of the brachiocephalic trunk2,3. However, CPB is accompanied by a high incidence of neurological complications4, among which stroke is a major factor5,6: Generally, a 6% prevalence of stroke after cardiac surgery is reported7, of which 2–3% are associated specifically with CPB8,9. This number increases remarkably towards 13% in high-risk patients10.

Among the main reasons for stroke are diminished brain perfusion, caused by impaired cerebral autoregulation (CA)11, and embolisms of released atherosclerotic plaques. CA describes the ability of the brain to stabilize the cerebral perfusion on healthy levels (80–150 mmHg)12 under different blood pressures and cardiac stress situations. An intact CA regulates cerebral perfusion via vasodilation and vasoconstriction depending on the cerebral metabolic demand and thereby prevents ischemia as well as hyperemia13. However, the CA mechanism is impaired in up to one out of five patients during CPB14,15. Reasons for this are assumed to be a decreased cerebral perfusion16 in combination with hemodilution and hypercapnia17 as well as the rewarming phase during CPB18. Patients suffering from impaired CA are generally more vulnerable to perioperative stroke16,19, therefore further increasing the number of high-risk patients. Further risks arise from embolism, either caused by thrombosis or the detachment of atherosclerotic plaque due to jet flow from the cannula on the aortic arch intima, which can dislodge atherosclerotic debris20.

Impaired CA and release of atherosclerotic plaque during CPB are both related to altered flow conditions in the aortic arch caused by the CPB outflow cannula. To avoid these two major complications, previous studies aimed to optimize the flow-pattern induced by the aortic outflow cannula. Two main approaches were taken: restoration of the naturally helical flow pattern in the aortic arch by inducing a helical outflow21,22,23 and deceleration of the outflow by dispersing the outflow jet20,22,23.

Here, we present an outflow-optimized cannula design (optiCAN), which was improved using computational fluid dynamics (CFD) simulations, prototyped and tested in vitro as well as in vivo. Our approach merges the suggestions mentioned above to optimize the aortic arch flow pattern: A helix in the cannula body ensures helical flow in the aortic arch and a flow-optimized cannula tip as well as an additional side hole at the tip decelerates the outflow. Both design features are suggested to increase brain perfusion during CPB. Furthermore, the side hole generates a flow in the ascending aorta, which may prevent possible stagnation regions. Based on in silico predictions, we finally present a further design optimization with manufacturing advantages.

## Results and discussion

With in vitro, in vivo and in silico tests, we compared the optiCAN design, which includes an innovative tip and a helical flow inducing inner wall geometry, with a commonly used curved tip aortic cannula (henceforth called reference). Both cannulas had a 24 Fr diameter and the same shaft length. A photography of both cannula designs is shown in Fig. 1a.

The following sections are divided in three fields of analysis: First, we discuss the results from in vitro testing, followed by those from in vivo testing and then present in silico results from numerical simulations based on the former two analyses. From the synopsis of all three analyses, we discuss limitations and future perspectives in the last section.

### In vitro testing

In vitro testing comprised qualitative outflow jet analysis, pressure loss analysis and hemolysis testing following ASTM and ISO standards with respect to sampling frequency and hematocrit values24,25. Details on each procedure are listed in the “Methods” section. The in vitro testing results are summarized in Fig. 1.

The results of qualitative cannula flow are depicted in Fig. 1b and demonstrate a widening of the outflow jet at the cannula tip: the dispersion angle increases by 64% from 25° for reference cannula to 41° for optiCAN design. Additionally, the outflow jet at the hole, intended for additional flow deceleration and aortic root washout (cf. Fig. 3a), yields a dispersion angle of an additional 18°. Under the assumption that these angles serve as indicators for the dispersion capability of both cannulas, the optiCAN design generates a twice as high dispersion compared to the reference cannula. Such increased diffusion lowers the outflow velocity as confirmed in silico by CFD simulations for a cannula design with a similar inner wall geometry21 as well as in vitro by particle imaging velocity (PIV) measurements on dispersion-tip cannulas26. Furthermore, such increased dispersion will make optiCAN less susceptible to positioning effects and misplacement during CPB in comparison to other curved tip cannulas, which could simplify cannulation handling during surgery (s. in vivo testing, below).

The pressure loss across the cannula rises similarly with increasing flow rates for both the optiCAN design and the reference cannula, as depicted in Fig. 1c. However, the optiCAN design shows a slightly higher pressure loss at all flow rates, reaching just over 40 mmHg at 6 L/min (reference: 38 mmHg). From the two design modifications, helix and tip design, it can be assumed that the former increases, while the latter decreases pressure loss compared to the reference cannula. Thus, both effects presumably counterbalance each other in the optiCAN and lead to similar pressure loss values for both cannulas. Furthermore, with a maximum pressure loss of about 40 mmHg at 6 L/min, optiCAN can compete with other angled dispersion-tip cannulas: its pressure loss is comparable to that of Optiflow™ (LivaNova®; approx. 40 mmHg27) and well below those of EZ Glide® (Edwards Lifesciences®; approx. 60 mmHg28) and Soft-Flow™ (Medtronic®; 62 mmHg29).

Normalized index of hemolysis (NIH) results from hemolysis testing are averaged over 2 independent examination days (s. Supplementary Information S1). After initial fluctuations in the first 1.5 h of testing, the NIH values stabilizes between 0.015 mg/dL and 0.02 mg/dL for all three setups, as seen from Fig. 1d. Generally, the NIH values of all circuits are comparable with each other within their standard errors: The high variances for optiCAN reflect higher uncertainty in NIH measurements, presumably caused primarily by the blood pump and components like connectors and clamps, as seen from the NIH value for baseline measurements. This is in line with previous studies, which report an NIH value of 0.03 mg/mL for the pump used in the present study30. Therefore, the hemolytic potential for both cannulas can be considered equivalent. For a quantitative comparison, we use the stabilized NIH values at $$t=6$$ h, confirming equivalent NIH values for reference and optiCAN design (s. marked values in Fig. 1d). From comparing these values, we derive that optiCAN does not induce additional hemolytic risks compared to the reference cannula within our experimental setup.

In summary, the in vitro testing shows that the optiCAN design reproduce the performance and hemocompatibility of a common reference cannula (cf. Fig. 1c,d), i.e. no alteration of hemolysis for optiCAN compared to the reference. In addition, the optiCAN tip design and side hole increase the dispersion of the cannula outflow compared to the reference cannula (cf. Fig. 1b).

In vitro experiments were furthermore complemented by in vivo tests to incorporate the biological mechanism of a living system.

### In vivo animal trial

In vivo tests were carried out in swine to measure the carotid artery flow provided by reference and optiCAN cannula during CPB (s. Fig. 2a). Carotid flow serves as a proxy for the cerebral perfusion as it accounts for the majority of the cerebral flow and effects, which increase carotid flow, generally also increase vertebral artery flow31. The physiological carotid flow under normal heart activity was measured as $${Q}_{0}=(1033\pm 30)$$ mL/min. Using the reference cannula under CPB, the perfusion dropped to $${Q}_{R}=(771\pm 94)$$ mL/min (74.6% of physiological value $${Q}_{0}$$), as shown in Fig. 2b. In contrast, when using the optiCAN cannula, the carotid flow was almost unchanged under CPB measuring $${Q}_{\mathrm{opti}}=(1007\pm 21)$$ mL/min (97.5% of $${Q}_{0}$$). In both cases, the cannula flow was equal to the previous cardiac output of 5.16 L/min. These results confirm our assumption that the restoration of the helical aortic arch flow pattern and the deceleration and increased dispersion of the outflow preserve physiological flow in the carotid arteries. The findings from the animal trial are consistent with previous results from in silico22 and in vitro studies23.

It is well known that a decreased cerebral blood flow during CPB increases the risk of postoperative neurological complications (PNC) in general and of ischemia in particular13,32. Assuming a linear relationship between cerebral blood flow and brain oxygenation (in first approximation)33, this would correspond to a cerebral oxygen desaturation of 25% during CPB from the drop in CBF for the reference cannula, which in turn is well known to again increase the risk of PNC caused by hypoperfusion34. From clinical experience, we know that the majority of patients treated with CPB are above 60 years, many of which have cardiovascular comorbidities (e.g. variable degree of stenosis of their carotid arteries, ranging from 20 to 80%), making them especially vulnerable to cerebral hypoperfusion during CPB. For these high-risk patients a restoration of cerebral perfusion towards physiological level is especially valuable.

However, as we are aware of the anatomic differences between swine and humans and of the individuality of each anatomy, we discuss the applicability of our findings from in vivo testing for clinical application in humans in the subsequent section.

### Numerical simulations

As an established and versatile proof-of-concept method, CFD simulations are employed here to answer three crucial questions for advancing optiCAN technology towards clinical application with reduced risks during CPB:

1. (1)

Within which limits can the results from in vivo tests in swine be applied to humans?

2. (2)

What risks of embolism from thrombus formation or plaque detachment during CPB are yet to be expected? In other words: How does optiCAN perform regarding hemodynamics, in particular regarding potential regions of stagnating blood flow or regarding the impact of the outflow jet on the aortic arch intima?

3. (3)

How can the design be further improved to overcome possible drawbacks regarding hemodynamics and manufacturability?

Question 1 addresses the need for further evidence on the effectiveness of the optiCAN design, before clinical trials in human patients can be arranged. By answering this question, we probe the “Validity of the animal model” presented in “In vivo animal trial”. Question 2 discusses cannula design criteria for evaluating those hemodynamics parameters, which are neither accessible by in vitro nor in vivo tests. Question 3 aims to optimize the current optiCAN design towards safe clinical applicability and cost-effective manufacturability (as a disposable medical device, optiCAN must be able to compete in manufacturing costs). We answer this question in the last subsection by proposing a “Further design optimization”, called opti2CAN.

#### Validity of the animal model

As the aortic arch anatomy of swine differs from human anatomy, we use CFD simulations to investigate the blood flow and distribution in an MRI-derived human aorta arch (s. “Methods” section for details). As shown in Fig. 3a, the streamlines of the cannula outflow in the aortic arch show the helical flow induced by the optiCAN design, as well as the rear flow through the additional hole. Both effects are absent for the reference cannula. While the pattern caused by the optiCAN inflow shows clear helical features from the insertion location onwards, the reference cannula ejects a jet flow, which passes the supra-aortic branches and hits the intima just distal of the left subclavian artery junction. Hence, optiCAN better reproduces the physiological helical flow pattern that was identified as beneficial in previous studies35.

When quantitatively comparing the cerebral flow simulated for either cannula with each other, the impression is confirmed: As seen in Fig. 3b, carotid artery flow is 87.7% of the physiological value for optiCAN and 68.8% of the physiological value for the reference. Flow in the vertebral artery is preserved better than in the carotid artery by both cannulas. Therefore, both cannulas show a slightly better performance when considering the overall cerebral flow (carotid plus vertebral artery): optiCAN preserves 89.4% of the overall physiological cerebral flow, while the reference reaches 71.2%. This yields an absolute increase by 18.2% (or 25.6% in relation to the reference) of cerebral perfusion from carotid artery flow using optiCAN. An even higher increase in cerebral perfusion is predicted from including vertebral artery flow (s. Supplementary Information S2 for details). It reproduces the same trend as observed in in vivo testing (even though not as pronounced; in vivo: optiCAN: 97.5%, reference: 74.6%; absolute increase of flow by 22.9% or 30.6% relative to the reference). These quantitative differences between in vivo and in silico results were expected based on two main differences between in-vivo test (with swine) and simulation (human): First and foremost, the anatomy of the aortic arch was different: Swine usually have two supra-aortic branches, where humans have three instead. The simulation was performed with a model of a human aortic arch with three branches. Second, a stiff aortic arch wall was modeled in the simulation, whereas it was flexible in the in vivo testing. However, assuming a stiff aortic wall as used in CFD simulations may even better resemble the situation in high risk patients, as a rather stiff aortic wall is expected for elderly patients and those with cardiovascular disease36. Despite potential anatomic differences, the CFD simulated predictions can therefore be considered to approximate the human reality, further substantiating the flow optimization effect of optiCAN.

#### Hemodynamic criteria

Regions of stagnating blood flow with velocities v < 0.01 m/s are prone to thrombus formation37. This can even occur for well heparinized patients, if the heparin boluses do not advance into the respective areas due to stagnating blood flow. When occurring in the ascending aorta during CPB, this effect may lead to thromboembolism, once the aortic cross-clamp is removed. In order to estimate the risk for this to happen, we identified regions of stagnating flow (velocity v < 0.01 m/s) as depicted in Fig. 3(a, shaded volume). A low-flow region is only observed in the reference cannula and is situated in the ascending aorta. The positioning of the cross-clamp is highly dependent on patient-specific anatomy and the individual surgical situation, and the aortic geometry used in the simulation moreover simplifies the blockage caused by the clamp. Therefore, the results are merely indicative and cannot be interpreted quantitatively. However, it can be noted that the rear flow from the side hole in optiCAN helps to prevent stagnation, as confirmed by the absence of such regions for optiCAN in Fig. 3a.

Wall regions exposed to the high flow velocities of the cannula outflow jet, on the other hand, are prone to the detachment of atherosclerotic plaque during CPB38. The impact of the jet on the intima can be quantified by wall shear stress (WSS), with high WSS values occurring in regions of strong impact. The buildup of plaque varies over the aortic arch and is more likely to occur in regions of chronically low WSS38,39. Regions of strong impact and those of increased risk for plaque formation under physiological conditions are depicted in Fig. 4. In accordance with Assemat et al., these regions of low WSS in physiological conditions are located at the onset of the supra-aortic branches as well as at the small curvature of the aortic arch38 (depicted in Fig. 4a). Under CPB, both cannulas increase the WSS in specific regions depending on their outflow characteristics, which is shown in Fig. 4b,c. Using the reference cannula, the left lateral side of the left subclavian artery junction is affected by high WSS, while using optiCAN it is rather the posterior ascending aorta and posterior distal aortic arch that are affected by high WSS, due to the different tip designs. Thus, the outflow jet of the reference cannula generates high WSS at a location more prone to plaque buildup (left subclavian artery junction; cf. Fig. 4a,b), thereby increasing the risk of plaque detachment. The outflow jets of the optiCAN, on the other side, generate high WSS at locations less prone to plaque formation (posterior aortic arch wall, cf. Fig. 4a,c).

For a quantitative analysis, we calculated the accumulated area of the aortic wall, in which the time-averaged (s. “Methods” section) WSS exceeds a certain WSS threshold. Since it is yet unclear, which WSS values are critical for detachment of atherosclerotic plaque from the aortic intima40,41,42, WSS thresholds were arbitrarily chosen ranging from 10 to 70 Pa, with 10 Pa step size as shown in Fig. 4d. The area of WSS exceeding these threshold value decreases in an exponential fashion with increasing threshold value for both cannulas. For WSS thresholds of 20 and 30 Pa, the reference cannula shows a larger accumulated area of WSS exceeding that threshold. For WSS thresholds of 40 Pa and above on the other hand, optiCAN shows a larger area with WSS above the threshold. The situation is yet different for the smallest threshold value of 10 Pa, where the area for optiCAN surpasses that for the reference, which results from the widened dispersion angle of optiCAN including the side hole outflow (cf. Fig. 1b) that naturally increases low WSS impact on the intima.

Such detailed and quantitative hemodynamic considerations complement results for commercially available perfusion-improved arterial cannulas for CPB: For example, pressure loss data are available for the Optiflow® by LivaNova®27, Soft-Flow® by Medtronic®20 and EZ Glide® by Edwards Lifesciences®28 (on the market) as well as the Stealthflow cannula (published in vivo studies43,44). An interesting approach was chosen recently by Gennari et al., who report microembolic signals (MES) during CPB in 19 out of 30 patients45. The study compares Optiflow® and EZ Glide® cannulas and doesn’t find a significant difference regarding tendency for MES (Optiflow®: N = 10, EZ Glide®: N = 9). The authors furthermore find that MES increase most during the actual aortic cannulation event and the start of CPB as well as opening of the cross clamp. It would be interesting to complement such in vivo examinations with CFD simulations to examine the potential relationship between WSS and MES with special focus on changes in the surgical procedure (i.e. cannulation, start of CPB, etc.).

#### Further design optimization

Based on the results presented in previous sections and prototyping experience, we identified the following design optimizations of optiCAN: (1) lower pressure loss over the cannula (cf. Fig. 1c), (2) lower WSS on the aortic arch intima (cf. Fig. 4d) and (3) competitive (cost-effective) manufacturability of the new cannula design. All of the above under the precondition that the potential optimizations must not compromise on the improved cerebral perfusion. Since the inner wall geometry of the cannula shaft was assumed to cause both an increased pressure loss as well as manufacturing difficulties, the helix was replaced by a shortened double-blade-version (s. Fig. 5a). The flow-splitting and dispersing tip design was retained. CFD simulation results confirmed less pressure loss for opti2CAN, (s. Fig. 5c): The pressure loss of opti2CAN is constantly below that of optiCAN, presumably due to the shorter helix length. Its pressure loss is furthermore below that of the reference cannula, allowing higher maximum flow rates during CPB. This takes advantage of the decreased pressure loss caused by the dispersion-tip with additional side hole as discussed above (cf. “In vitro testing”). The simulations show the same tendency regarding the difference between pressure loss of optiCAN and reference cannula as the in vitro tests (cf. Fig. 1c). Quantitatively, the discrepancy between in silico and in vitro results regarding the pressure loss difference between the two cannulas is 0.63 mmHg on average. This is probably due to manufacturing inaccuracies, which are not represented in the simulation. Notice furthermore, that the absolute pressure loss values are constantly lower in the simulations, since the shafts of all cannulas were shortened to reduce simulation time.

The cerebral flow provided by opti2CAN was predicted in the same way as described in “Validity of the animal model”. As the results in Fig. 5d show, opti2CAN performs just as well as optiCAN, restoring 87.1% of the carotid artery flow during CPB. From this, one can assume from CFD analysis that the opti2CAN design will preserve physiological flow during CPB equally well as the in vivo tested optiCAN design, thereby restoring physiological cerebral flow and meeting the proposed design precondition.

Concerning the hemodynamic criteria of as low as possible area of high WSS, opti2CAN revealed a substantial improvement. The accumulated area of high WSS was constantly below that of both the reference and optiCAN design (Fig. 5b,e). In particular, the area with WSS above 50 Pa effectively decreased to zero (i.e. zero affected area), whereas the same occurs for reference and optiCAN only for WSS > 70 Pa. This shows lower impact on the aortic arch intima and gives reason to assume a decreased risk of plaque detachment during CPB using opti2CAN.

Along the way, the manufacturing process was simplified by going from precision gluing the inner helix geometry for optiCAN, to potentially manufacturing opti2CAN in one piece by means of injection molding making gluing redundant. This could not only reduce manufacturing time and cost but would also increase reproducibility and simplifies quality control for large scale manufacturing.

#### Limitations and future work

In silico pressure loss results are in good agreement with in vitro pressure loss measurements (cf. Figs. 1c and 5c) and in silico cerebral perfusion results are consistent with those acquired through in vivo testing (cf. Fig. 3b. Overall the cerebral perfusion could be restored to physiological flow with our proposed cannula designs. While acknowledging these findings for advancing safe CBP treatment in general, we are aware of the following limitations of our approach from which we derive future research perspectives as follows:

Albeit good agreement between in vivo results from CPB in swine and in silico results from CFD simulations in a human aorta was achieved, statistical result validation is warranted by systematically repeating our CFD analysis in multiple human anatomies and additional in vitro and in vivo experiments. In this process, prototype testing of the improved opti2CAN cannula geometry is also intended. Additionally, porcine aortic arch anatomies could then be extracted from the test animals and subsequently used in CFD simulations to allow for more accurate case-by-case validation of our CFD model.

Measures were taken to ensure identical placement of the cannulas during the animal trial (s. “Methods”). The impact of placement variations (for example angle of the cannula tip relative to the aortic wall and positioning of the cannula relative to the aorta) will be investigated both by additional in vivo as well as in silico investigations46. Furthermore, different human aortic anatomies could be analyzed. Both cannula placement and aortic anatomy will most likely influence WSS distribution as well as the perfusion of the supra-aortic vessels, because they have an influence on factors like distance between cannula tip and aortic wall, distance from cannula tip to supra-aortic vessels and angle of the inflow jet relative to the aorta. Furthermore, it is assumed that dispersion-increased designs (like optiCAN and opti2CAN) are less susceptible to position effects on cerebral perfusion than regular straight-tip designs—this remains to be validated, however.

For stable patients with negligible pressure changes, constant boundary conditions and constant cannula flow can be considered an adequate approximation for the CPB setting. For a more accurate prediction of physiological flow, transient simulations with pulsatile boundary conditions can be evaluated in future simulations studies.

Finally, optiCAN and opti2CAN should be tested against other dispersion-tip cannulas such as Optiflow®, Soft-Flow® and EZ Glide®, in order to assess their suitability for competitive clinical application.

## Conclusion

We analyzed a flow optimizing aortic cannula design (optiCAN) intended for use during CPB. In vivo testing in swine revealed a restoration of cerebral perfusion to 97.5% of the physiological flow during CPB, which is attributed to a helical outflow and dispersion-induced deceleration. The preserved cerebral flow reduces risk of oxygen desaturation during CPB and of PNC after CPB considerably, while pressure loss and hemolysis were not considerably higher compared to reference. Furthermore, the increased dispersion provides easier positioning of the optiCAN during surgery as compared to other curved tip cannulas and decreases the risk of misplacement of the cannula due to patient movement. These results from in vivo can be reproduced in silico for an exemplary human aortic arch employing CFD simulations. Here we showed that the optiCAN jet-splitting and dispersion tip design nullifies blood flow stagnation areas in the ascending aorta, remedying the risk of thrombus formation during CPB. Within the CFD framework we further assessed the risk of embolism during CPB due to plaque detachment by comparing the strength and distribution of WSS on the aortic arch intima: We reveal that areas of high WSS is slightly increased for optiCAN compared to the reference cannula, making both susceptible to plaque detachment potentially causing embolism. We demonstrate via CFD simulations that the area of high WSS as well as pressure loss of the optiCAN design can be reduced by introducing another design optimization (opti2CAN), which incorporates a helical-flow-inducing double-blade geometry in the cannula shaft. At the same time, opti2CAN preserves physiological flow for cerebral perfusion during CPB in the same way as optiCAN. Thereby, opti2CAN design could reduce the prevalence of stroke and embolism in high-risk patients at the same time, while adding improved manufacturability due to simplified assembly. With this we present a novel design option to reduce cannulation side effects during CPB and hope to contribute to improved therapy options for high-risk patients.

## Methods

### Cannulas for in vitro and in vivo experiments

The reference cannula was a 24 Fr curved tip aortic cannula in the style of, for example, PureFlex™, Curved Tip (LivaNova plc, London, Great Britain), DLP™ Curved Tip (Medtronic plc, Dublin, Irland) or Medos angled tip arterial cannula (Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany). The optiCAN tip and helix were designed based on previous CFD-optimization iterations22, but improved by reducing the tip diameter at the insertion site equal to the reference cannula to ensure simple and safe placement. Tip and helix prototypes were manufactured by injection molding and attached to, respectively precision glued into, the wire-enforced silicon cannula body of a commercial standard cannula from which the tip had been removed. This ensured that the cannulas differed only in terms of their tips and the inner wall geometry.

### Ink-stained outflow-jet analysis

For ink-stained jet-flow analysis, the respective cannula was connected via silicon tubing to a pump with adjustable steady flow between 1 and 6 L/min. The outflow cannula was placed in a water reservoir allowing for steady outflow with the tip well under water. At defined time steps, 10 mL of commercially available blue ink (4001 Blue Ink, Pelikan Holding AG, Schindellegi, Switzerland) were added to the inflow to visualize the outflow-jet. The ink-stained outflow-jet was recorded on video (1920 × 1080 pixels at 30 FPS) and analyzed in series of single pictures.

### Pressure loss analysis

For the pressure loss analysis, the cannulas were connected to a centrifugal pump via silicon tubing, while the cannula tip was placed in a water reservoir allowing for steady outflow with the tip well under water. The flow was regulated to steady rates of 1, 2, 3, 4, 5 and 6 L/min, monitored by by SonoTT™ Clamp-On Transducers (em-tec GmbH, Finning, Germany). The pressure was measured at the luer lock connector of the cannula, at approximately 22.2 cm distance to the cannula tip, by Xtrans® pressure sensores (CODAN Medizinische Geräte GmbH & Co KG, Lensahn, Germany).

### Hemolysis analysis

Hemolysis tests were performed with heparinized porcine blood on two independent test days following ASTM and ISO standards: three cannulas (2 × optiCAN, 1 × reference) were tested in parallel, each in an individual blood circuit with approx. 520 mL priming volume. For reference, a fourth circuit without a cannula (baseline) was also examined. Each circuit was operated at (5.12–5.16) L/min by a roller pump (Jostra, formerly Maquet Cardiopulmonary AG, Hirrlingen, Germany), their parts connected by 3/8″ PVC tubing (Raumedic AG, Helmbrechts, Germany) and kept at constant temperature of 37 °C. The hematocrit was determined using a hematology analyzer (Celltac Alpha MEK 6450 K; Nihon Kohden Europe GmbH, Rosbach vor der Hohe, Germany) and the flow rate was measured by a SonoTT ultrasonic flow probe (Em-tec Flow Technology LP, Berkshire, NY, USA) attached to the tubing. Porcine Hematocrit was stabilized at $$Ht=\left(12\pm 1\right) \mathrm{g}/\mathrm{mL}$$25.

4 mL samples were taken following ASTM and ISO standards24,25 from the circuits at times $$t=(\mathrm{15,30,45,60,90,120,180,240,300,360})$$ min, from which the plasma free hemoglobin ($$fHb$$) was obtained using an Ultrospect 2100pro photometer (GE Healthcare Europe GmbH, Freiburg, Germany). The normalized index of hemolysis (NIH) was determined from the difference in plasma free hemoglobin between two measurements ($$\Delta fHb$$), the duration between consecutive sampling ($$T$$) as well as circuit volume ($$V$$), and flow rate ($$Q$$):

$$NIH \left(\frac{g}{100 L}\right)= \frac{\Delta fHb}{T}*\frac{V*(100-Ht)}{100*Q}$$

### In vivo animal trial

This study was approved by the KU Leuven animal ethics committee (P157/2011) and was performed in accordance with relevant guidelines and regulations The animal received humane care in compliance with the ‘Principles of Laboratory Animal Care’ formulated by the National Society for Medical Research and the ‘Guide for the Care and Use of Laboratory Animals’ prepared by the Institute of Laboratory Animal Resources (National Institutes of Health). Where applicable, authors complied with the ARRIVE guidelines (only one in-vivo test on one animal was performed).

One swine (66.7 kg, aged 4.2 months) was included in the study and received general anesthesia (intramuscular, tiletamine/zolazepame—8 mg/kg, xylazine hydrochloride—2.5 mg/kg) at the initiation of the procedure. Anesthesia was maintained by continuous administration of propofol (10 mg/kg/h) and fentanyl (0.15 mg/kg/min). After intubation, the animal was mechanically ventilated with the use of a volume-controlled respirator (Cicero, Drägerwerk AG & Co. KGaA, Lübeck, Germany). For rhythm stabilization, animals were primed with 300 mg amiodarone and 0.5 mg/kg/h lidocaine was continuously administered. A protective mechanical ventilation was maintained while on CPB, including pressure-controlled ventilation at an I:E ratio of 2:1, a respiratory rate of minimally 6/min with an FiO2 of 0.25, low tidal volume (6 mL/kg body weight) and high PEEP (≥ 10 cmH2O). During the entire experiment, phenylephrine was the only vasoactive/inotropic medication allowed to maintain a mean arterial pressure above 50 mmHg.

A median sternotomy was performed and the heart was exposed in a pericardial cradle. After administration of heparin and placement of purse strings on the ascending aorta and right atrium, an arterial cannula was placed in the ascending aorta and a venous cannula in the right atrium and CPB was started. Aortic cannulation was performed first with the reference cannula and then with optiCAN. To ensure that the positioning and tip orientation was the same for both cannulas, they had identical suture flanges and sutures were placed at the same locations. The flow rates at the two carotid arteries were monitored with a 5 mm QuickFit TTFM Flow Probe (Medistim ASA, Oslo, Norway).

### Computational fluid dynamics simulations

Computational fluid dynamics (CFD) simulations were conducted to compare the optiCAN design with a common aortic cannula regarding their influence on the hemodynamics. The hemodynamic parameters analyzed were pressure loss across the cannula, areas of increased wall shear stress, low flow regions and distribution of blood flow to the upper branches of the aortic arch.

Two simulation setups were used: For the pressure loss analysis, the cannulas were placed in a model of a reservoir to mimic the in vitro pressure loss measurements (reservoir setup, (cf. Figs. 1b, 5c),). For the analysis of all other parameters, a model of a human aortic arch with and without cannulas was used (aortic arch setup, cf. Fig. 3). The geometry of the aortic arch was segmented from magnet resonance imaging data of a healthy 28-year-old male volunteer. A clinician confirmed the correct positioning of the cannulas. All geometries were prepared using 3-matic (Materialise NV, Leuven, Belgium).

The simulation meshes were created with Ansys ICEM CFD 2019 R3 (Ansys Inc, Canonsburg, US). Tetrahedral elements plus 14 prism layers with a first layer height of 0.005 mm were used. A mesh sensitivity study was conducted (see Supplementary Information S3).

The CFD simulations were performed using Ansys CFX 2019 R3 (Ansys Inc, Canonsburg, US). The reservoir setup was implemented as a steady state simulation with a constant cannula flow rate (1, 2, 3, 4, 5 and 6 L/min) and ambient pressure boundary condition at the walls of the reservoir. Water with a density of 997.0 kg/m3 and dynamic viscosity of 0.8899 mPa*s was used to facilitate the subsequent comparison with in vitro experiments.

For the aortic arch setup, a transient simulation setup with constant boundary conditions was chosen, since the short distance between cannula tip and aortic arch wall led to transient effects in the jet flow. A time step of 0.5 ms and a simulation time of 0.45 s were chosen. Cannula flow was set to 5.16 L/min, which corresponds to the average flow rate during the in vivo experiments. A wall was placed in the ascending aorta to model the aortic cross-clamp and a constant outlet pressure of 90 mmHg and a loss coefficient of 50 were defined for all remaining boundaries. The loss coefficient was deduced from the Darcy-Weisbach equation for friction-related pressure loss in a previous study22.

In both reservoir and aortic arch setup, blood was modeled using a shear dependent viscosity model based on Ballyk et al.47 (see Supplementary Information S4). A density of 1056.4 kg/m3 was chosen, which corresponds to a hematocrit of 44%. In both setups the SST turbulence model was applied, and the convergence criterion was defined by a maximum residuum of $${10}^{-4}$$. Additionally, pressure at the cannula inlet (reservoir and aortic arch setup) as well as average WSS and mass flows at the outlets (aortic arch setup) were monitored to ensure stabilized predictions and variations well below 3% over the final 100 time steps.

Post processing was performed using CFD-Post 19.3 (Ansys Inc, Canonsburg, US). The aortic arch setup was analyzed using variables that were averaged over the last 100 time steps.

Pressure loss was defined as the difference between predefined wall pressure of the reservoir and pressure at the cannula inlet, mimicking the experimental setup. WSS values were automatically calculated by the CFD-Post formulation, which is provided in Supplementary Information S5.

Further details of the meshing and simulation setup can be found in the Supplementary Information S3 and S4.