Dr. Chester Kyle : Pioneer in Human Powered Vehicle Aerodynamics

Chester Kyle, PhD is a pioneer in cycling science and the aerodynamics of human powered locomotion. Now retired, he served as an adjunct professor of mechanical engineering at the U of California at Long Beach for several years. He also served a stint at Nike designing aerodynamic clothing. 

When you read the breadth and scope of his work, you really will be surprised how much of aerodynamic design and technology that has infiltrated cycling, whether real or gimmick, had been already explored by him and his students. From drivetrain efficiency to the aerodynamic drag of a shaved vs unshaved cadaver, there was practically little he didn't explore and write on.

Several ideas introduced by him and his peers at the US Olympic committee and human powered vehicle circles went onto win international cycling championships and setting speed records. At the height of his career, the UCI was hot on the chase of these "ideas", banning several of the improvements broguht to the UCI sanctioned cycling races. As Dr. Kyle likes to joke, 'the minute they saw them [plop], they became illegal'.

Dr. Kyle's investigations into technical aspects of cycling through simple experiments driven by a sense of economics and curiosity for the science should serve as an example for any investigator today. The most important aspect of these investigations was that he helped produce a body of empirical data that let other researchers and designers go about their business.

On 13th May 2010, he was invited to a seminar for the students of MAE297.  The following is the video from that lecture. An amazing little talk, and made no worse by Dr. Kyle's upbeat attitude and quirky sense of humor. 

Credits :
Film by the UC Davis Engineering distance learning program, 2010.

How the Paris Agreement Will Impact EU Climate and Energy Policies

A broad topic but a good discussion panel organized by Bruegel.

There appears to be the view that emissions targets and ETS will have to revised. More importantly, innovation in new technologies and renewables and how to integrate it with the existing infrastructure will be key in setting up planned targets for 2050. Natural gas will have a bridging role in the energy transition.

Apart from the EU Commissioner for Energy and Climate Action Miguel Arias Cañete, the panel also has a GE representative Hendrik Bourgeois who provides the investment requirements and investment risks coming along the way to low carbon. 

In short, despite the optimism from some circles, the engineering, economical and social challenges for a low carbon economy makes implementing the Paris Agreement an extremely complex task. Below is just the EU viewpoint. 

The EU's Roadmap for a competitive low carbon economy in 2050 can be read here.

Laws of Thermodynamics (In Simple English)

First Law: It is impossible to obtain something from nothing, but one may break even

Second Law: One may break even but only at the lowest possible temperature

Third Law: One cannot reach the lowest possible temperature

Implication: It is impossible to obtain something from nothing, so one must optimize resources

This was obtained from one of my thermodynamics reference books from college days. I'm amused at the plain language in the three maxims. A lot of new discussion surrounding energy systems can be cut short if you invoked any of these maxims in it's simplest form and then thought again. 

Citation :
Advanced Thermodynamics Engineering, by K. Annamalai and I. K. Puri, CRC Press.

An Equation for Running Stress Score (RSS)

On Stryd's website, there is a narrative about their proprietery scoring system based on power called Running Stress Score (RSS).

The key statement is how RSS is defined using a 'co-efficient' K.

Someone who would like to reverse engineer this formula would wonder if co-efficient 'K' is a constant or does it vary depending on the intensity.

One clue to help in finding K is a table of examples in which Stryd states an expected value of RSS.

Infact, from the equation of RSS, the value of K is defined as :

K = [ Natural log (RSS/min) - Natural log (100)] / Natural log (Power/CP)
where CP = critical power

One finds from this calculation that there is not a single value of K that can be fitted to the running examples in the table above. So either this is a small mystery or K is not constant.

Might there be an easier model to explain the change of RSS/min with intensity? As a first goto, a simple exponential model would reflect rapidly increasing stress scores for higher intensities.

So I took the data and tried to force fit an exponential line through. The result gave 98.7% fit based on the data fed to it.

Based on this exercise :

RSS/min = A x exp(B x Power/Cp)
where parameter A = 0.0758
and     parameter B =  3.1297

How does this equation fit with a real run and it's corresponding RSS from Stryd's powercenter? I took a recent run from the running database and threw it into the model.

I found that the modeled RSS/min is within 3% of the actual value, which says that the fit is alright but more importantly, I can produce a better match by decreasing assumed CP to around 193.5 W.

State of Energy Storage Methods

The crux of using intermittent renewable energy sources is how they integrate with flexible, conventional sources of energy to generate uninterrupted power. The link between both is the specific energy storage technology that is used. 

The state of storage technologies were reviewed by IEA 2014. Though it would be good to have an update, it is still a good resource to read as it reflects the current level of understanding of energy storage by mature countries in the IEA club.

An excellent database of storage plants around the world is maintained and updated by the U.S Department of Energy (Link). Engineers might want to head out there and learn the characteristics of some of these plants.

How Valid is Polar V800 HR to Measure HRV Metrics?

According to a 2015 paper by Giles et.al, the Polar V800 "improves" over previous generation Polar devices with narrower intra-class correlation coefficients, limits of agreement and effect sizes when compared to R-R readings obtained from a 3 lead Biopac ECG.

One of the key aspects of the paper is that it highlights the importance of understanding the effect of different softwares and their error correction routine on the R-R time series.  The paper references some earlier studies which flagged the concern that each system's individual HRV processing capability can potentially alter the HRV metrics in their own way making cross-comparability a big issue.

The paper discusses chiefly 7 errors that can be associated with the Polar V800 HRM signal. Potential factors leading to those errors are also stated. I'd like to borrow the learning and place them here :

Type of Error	Description of Error	Potential Cause
T1	Single interval of discrepancy	Not stated
T2	Long interval and short interval	Not stated
T3	Short interval and long interval	Not stated
T4	Too few intervals detected	Decrease or loss in contact between electrode-skin and resulting decrease in amplitude of the R wave
T5	Too many intervals detected	Not stated
T6-a	Interval(s) missed entirely, undetectable	Software error due to time asynchronicity in the HRM and/or Loss of, or decrease in contact between electrode-skin.
T6-b	Interval(s) missed entirely, detectable	Same as T6-a

Although the causes of T1-T5 errors are not addressed in the paper, the authors state that all of them may be recognized and corrected without the use of a simultaneous ECG recording. However, they highlight the fact that the most appropriate technique for correction of R-R time series is still pending proper research and agreement.

The combined error rate of Polar V800 in standing and supine positions was 0.086% which is a marked improvement over previous research findings from the Polar S810 (0.40%) and Polar S810 (6.93%). The paper concludes that the device is a "valid" tool for the detection of RR intervals "at rest" and improves on previous HRM models with regard to comparability against ECG. 

The meat of the paper is it's tabulated summary of means, bias (LoA), ICC (95% CI) and effect size, which I'd like to reproduce here. These numbers are the justification for stating the validity of the V800. An interesting area to note is the level of discrepancy in HF power between V800 and ECG in the supine position. It is an order of magnitude different, whereas much more agreeable in the standing position. Is this a typo or the actual finding?

Heart Rate to Power Ratio Metric for Running

Attached below are some squigglies of HR:Pw profiles from my own runs. Data indicates slope is very dependant on what the ambient conditions are. For instance, the body's challenge to cool is one fundamental driver of high HR. The body's ability to process oxygen at high altitudes is another. 

Long, slow, flat beachside run, hot and humid : First image is a long, slow controlled jog outdoors at 38 deg C, 50% humidity and medium wind for an effective temp index of 38. The squiggly goes straight up which is interesting. What this means is that under such hot conditions, prolonged exercise in low power still leads to gradual HR elevation or "drift". 

Treadmill run : Second image is from a 30 min easy run on a treadmill in an airconditioned gym. Power is limited to a tight range, run is well controlled and slope of squiggly is gentler than that of previous scenario.

Interval training, beachside, hot and humid : An example of 2 x 6 min, interspersed with 3 min slow running. 35 deg C, 50% humidity and medium wind for an effective temp index of 33. Key information - recovery jogs involved dousing self with water, on the head, down the back etc. During work segments, convection helps a bit to dissipate heat.

High altitude run : The last example is from a 40min run at high altitude, cool environment. Maximum elevation was around 11,800 ft and much of the running terrain was uphill, therefore power numbers are higher than equivalent runs done on flatland. This was the first run in such an environment, hence the body was coping with acclimitizing and effort can be classified oxygen limited.

The histograms on the x-y axes are another key piece of data. Skewness of the bars indicate frequency distribution at high and low ends of the heart rate or power spectrum.  

Altitude running when non-acclimatized is a case of high HR dominant running, where those bars are skewed to the higher numbers. For example, a predominantly dual peaked histogram for running power could point to an interval session, as the third image shows.

Another way to show the HR:Pw plot would be to express the numbers on the axis in terms of a percentage. This gives better visual cues on intensity, both to runner and a coach.

Apart from looking at slope, the area distribution of running data on a HR;Pw map might also be useful. At a conceptual level, the way datapoints can be arranged on a %HR:%Pw plot is depicted below by 3 types of colored dots. High HR:Pw runs in black and low HR:Pr runs in green. Red dots falling on the diagonal line might be an idealized scenario, where the increase in HR follows organically from an increase in running power.

Concluding Remarks

Heart rate to power ratio with frequency distribution information is one quick and easy way to classify the intensity of run. The advantage of the HR:Pw plot is that it captures whole body state of affairs (HR) and activity specific intensity (power). Ofcourse, the Hr:Pw profiles maybe slightly different for everyone, since everyone reacts a bit differently to temperature, humidity, altitude and stress. Looking at runs in this way also helps in controlling for ambient factors and in trying to compare apples to apples when analyzing performance across several runs.

Read more of my articles at Running Science.