The Falling Stick Problem

Here's a little problem for physics teachers, mechanics enthusiasts, slo-mo videographers, science communicators, and other curious onlookers. Let a straight and rigid stick stand vertically on a flat horizontal surface. Then, without imparting any initial velocity, release the stick and let it fall from standstill. As it falls, the angle it makes with the vertical keeps increasing. Let's call this the fall angle (ϕ). At what fall angle does the bottom of the stick move away from where it was originally placed? In other words, what is the take-off angle (ϕm)?

In this IOP Bioinspiration and Biomimetics paper from 2015, we were looking at this problem as part of a modeling exercise on human walking, with a simplistic view considering the human as a 2D inverted pendulum, like the falling stick above. In that model, using some basic mechanics equations (no rocket science involved), we predict that the take-off angle is about 48°, cos^-1(2/3) to be exact. This result holds irrespective of the mass and length of the stick or even which planet you're doing this activity on, a heart-warming general result. The figure below shows the take-off angle for four different leg lengths (0.4m, 0.6m, 0.8m, 1.0m). The x-axis (vo) denotes the initial velocity of the bob of the inverted pendulum. Ping me if you are interested to read the paper but cannot access it in the above IOP link.

 Here is a snippet from the paper.

"As the inverted pendulum falls, the compression in the stance leg keeps reducing. At the instant the compression becomes zero, the ground will need to start pulling on the rod. This is not possible (unless the foot is stuck on the ground due to some adhesive or suction pads) and hence the inverted pendulum takes off." ~L Patnaik and L Umanand

Without slo-mo videography―and with more important fires to put out in order to finish a PhD―we never really verified the 48° claim experimentally. I would be happy to know if someone has, or can capture, slo-mo video footage of this mundane phenomenon, to help find the answer. The underlying message is to showcase the amazing fact that simple mathematical models can often make reasonably accurate predictions about the world around us.

Curtains of Nature

Visiting the Swiss Alps in cloudy weather evinced some poetry in good old wet ink: photo of original here. I give you 'Curtains of Nature' from Celerina, Graubünden.

Peace, Oh traveller
Grudge not the weather
Fear not the clouds
As the curtains of nature

For even if you
Were not to revel
In their own tapestry
They let you focus
On the beauty of that
Which is visible

Each of your pictures
Is a half in colour
A half in grey scale
Showin' you the contrasts
That make your life

And as the curtains
Swirl with the wind
And change their forms
Play with the light
And change their colors
They keep unravelling
Latent layers of allure

Fear not the cold and dampness
For you often have the gear
To lessen the disquiet
Let nature be, just let her be

The lightbulb machine: How to come up with new research ideas

 

 

"It seems necessary to me, then, that all people at a session be willing to sound foolish and listen to others sound foolish." ~Isaac Asimov in his essay titled On Creativity

 

Most researchers are learners trying to understand their field, and beyond, as best as they can using the tools at their disposal. But of course research is about pushing the frontiers of knowledge a tad outside of what is already known to humanity. This is beautifully expounded as a dent in the circle of human knowledge in The Illustrated Guide To A Ph.D. by Matt Might, University of Alabama at Birmingham. Although Might's depiction shows this phenomenon in an academic context where the game is explicitly sytematised, a lot of people are (often anonymously) denting the circle outside of the typical grad school setting and without the fancy hats and badges. In our efforts to make those dents, we are always on the lookout for ways to generate new research ideas.

The central question then is: How can I come up with an idea that no human has come up with before? I cannot seem to will them into existence, ideas seem to come out of the blue, often at odd times. In most cases, after arriving on an idea, the initial feeling of triumph is promptly damped by a Google search that throws up a 1990s paper (hopefully not earlier) that already proposed the idea, and they probably went above and beyond what you had envisaged. No wonder, the key to having good and new ideas is to have many many ideas—harness the power of combinatorial success. If we need so many ideas, what are our methods for idea generation? I thought of compiling a few of my tricks here so I can come back to it when I am sans inspiration. May be some of these work for you too.

1. Combine two (or more) disparate ideas

Entrepreneur and leadership speaker Joel Hilchey says, "Many new ideas come from combining two distinct ideas. E.g. combining a phone and a computer, we have a smartphone." He has a point. The process of ideation is a lot like chemistry. Ideas are like atoms and molecules. We come up with new ideas by bringing together and combining existing ones. If you have a lot of 'elementary ideas' and you keep shuffling them around, rearranging them in different ways, ultimately some of them will naturally snap together to form new ideas. You can then further explore these newly formed compound ideas putting them under the microscope to discover their properties and potential. Some tools that facilitate this task of continuously rearranging ideas are writing, making tables and lists, drawing graphs and curves (by hand AND by computers), mind-maps, sorting your notes by keywords (Tiddlywiki is excellent for this), and of course the good old Socratic method with an honest interlocutor.

Another good way of ensuring regular combinations of disparate ideas is for an individual to have at least two fields of study, say a major and a minor, and to keep smuggling ideas from one field to the other. As an engineer, a clear manifestation of this method is 'from science lab to engineering lab'. Take recent scientific advances and use them in engineering applications. These could be new devices, components, materials, configurations etc. E.g. Robert Middlebrook took William Shockley’s transistor idea, and used it in circuits to spawn the area of power electronics—which is central to technologies like renewable energy, electric vehicles, and smart grids today. This 'translation story' is laid out in illuminating detail in a 1998 Middlebrook interview by K. Kit Sum. Another stalwart in power electronics, Fang Z. Peng from Michigan State University, sometimes recruits grad students with absolutely no background in power engineering citing the rationale, “Great ideas in a particular research area come from outside the field.”

We've been focusing on bringing together separate fields of study in our quest for new ideas. Sometimes, however, theoretical and practical aspects of the SAME field can get siloed into being like separate fields. This is where opportunity is rife for switching back and forth between the two sides in order to shuttle ideas. Make the divide between industry and academia porous. If you are not yourself able to switch sides, invite people over from the other side. Build bridges, open doors.

 

2. Marry complementary problems

A bad effect of one system can serve as a good effect for another system. Put them together to get integrated solutions. E.g. Cooling requirements are highest when it is sunniest i.e. the available solar energy is maximum. Hence explore solar-powered air-conditioning, and solar-driven peltiers for cooling photovoltaic cells. Food for storage needs cooling while a home, or at least its water supply, might need heating. Instead of investing resources separately into each of these problems, what is a good way to integrate their complementary needs into a single solution? I go crazy when I see my refrigerator working hard to keep my ice-cream frozen while the room-heater tries to heat the space around the refrigerator.   

Sometimes, it is easier to solve multiple problems with one solution. E.g. shifting to a largely active transportation model, à la Amsterdam, consisting of short-distance trips of walking and biking simultaneously addresses the issues of air pollution, public health, road accidents, and climate emergency. This is the kind of problem solving approach that Elizabeth Sawin calls multisolving. Like the bridging approach of #1 above, multisolving also entails talking to researchers in other fields about the key problems they are trying to solve.

Another way of looking at multisolving is to “overload” existing systems. If something already exists, what can it do in addition to what it was designed to do? Sometimes, with just small modifications, we can make existing systems and components do additional tasks. E.g. (1) Using WiFi to serve as an indoor GPS; (2) Using the motor-drive power converter circuit of an electric vehicle as battery charger.

 

3. Measure everything, then infer

New eyes. This is what a rich variety of sensors and instrumentation allow us today. Observe the system under study from different perspectives, then look at the data to hopefully tell things about its health and surroundings that were previously unknown. Often this involves bringing in types of measurement instruments not typically associated with your system under study. E.g. Electronics engineers are used to probing circuits with multimeters, oscilloscopes, and (for the wealthy ones) spectrum analyzers. How about microphones? Based on audible sound signature of a motor or other electro-magnetic device, can we infer something about the health and/or operating mode? A related tip for young electronics engineers from ISRO's Manoj R. Iyer is to use current probes as we often tend to get locked into using only voltage probes seeing only voltage waveforms. Taking this new-eyes idea into creepy territory, MIT researchers found a way to decipher what someone is speaking based on the vibrations on a Lay’s chips packet near the person.

 

4. Classify and tabulate

I briefly brought this up in #1 above, but the classify-and-tabulate method probably merits a separate mention on its own. This has been a powerful tool from the beginning of science as many humans derive cathartic pleasure in arranging things systematically (Marie Kondo likes this) and then looking for hidden patterns. Exhaustive classification and tabulation provides a bird’s eye view of the field and easy comparison of normally scattered pieces of information by the simple act of juxtaposition. A table tells us what are the things we know quantitatively and what we know qualitatively. A table is a powerful tool for locating gaps in knowledge as depicted below. This is perhaps why you would find lots of tables in technical texts even though we have more aesthetic tools like graphs and plots.

 

5. Draw waveforms and plots by hand

With the ubiquitous computers and simulation tools, it is easy to let them do all our plotting. While that is widely used for good reason, I would draw attention to drawing waveforms and other plots by hand. Drawing by hand makes you think in ways that simulation doesn't, simply because the latter is sometimes a bit like watching a football match rather than playing it. Drawing by yourself is akin to running your own mental simulation. Let us say you try to draw an XY plot. Immediately you have to first label the axes and think about the typical range of numbers for each axis for the chosen units. As you put down the pen on paper, where do you start and end the curve? What are the initial conditions and boundary conditions that define the constraints for what you draw. What is the slope in different parts of the curve? You will need to know about dynamics, rate of change, and sensitivity. Is the function monotonic, is it continuous, is it differentiable? As you put ink on the seemingly dead piece of paper, it comes alive with many questions. You are having a rich conversation with dead plant tissue.

6. Eye for detail

The prolific Isaac Asimov wrote these insightful lines: "The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but rather, 'hmm... that's funny...'" While it is easy to look for things that you want to see, seek out details that are not as per your expectation even if they are fleeting. That is where new ideas and potential problems hide. The Davis Dictum says, "Problems that go away by themselves come back by themselves." There is this category of bugs that show up only intermittently and are hard to reproduce. These types of bugs are the toughest little scoundrels to understand and debug. And often, their genesis lies in the ignored details that have always been there, albeit not always in plain sight. And so, keep an eye for detail not just in your mind put perhaps also in your notes and reports. Further expanding this 'tell everything, hide nothing' philosophy, Richard Feynman writes in 'Surely You're Joking, Mr. Feynman!':

“If you’re doing an experiment, you should report everything that you think might make it invalid—not only what you think is right about it: other causes that could possibly explain your results; and things you thought of that you’ve eliminated by some other experiment, and how they worked—to make sure the other fellow can tell they have been eliminated. Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can—if you know anything at all wrong, or possibly wrong—to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it. There is also a more subtle problem. When you have put a lot of ideas together to make an elaborate theory, you want to make sure, when explaining what it fits, that those things it fits are not just the things that gave you the idea for the theory; but that the finished theory makes something else come out right, in addition. In summary, the idea is to try to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgment in one particular direction or another. The first principle is that you must not fool yourself—and you are the easiest person to fool.”

7. Old books, new ideas

Research today tends to fall into the habit of limiting itself to recent references, and clean and searchable PDFs. There are many brilliant ideas in old books and documents. When I say old, I mean yellowed-pages old, possibly even tattered. Some of these ideas might have died because the context in which they came up was not conducive, or because there weren't tools to implement them. Delve into the archives, and this will perhaps also make you a steward for their preservation. This is why I have great respect for libraries, and online archives like Internet Archive and Project Gutenberg. In his book Chaos, James Gleick writes about physicist Albert Libchaber:

 

"His colleagues joked about his obsession with old books. He had hundreds of original editions of works by scientists, some dating back to the 1600s. He read them not as historical curiosities but as a source of fresh ideas about the nature of reality, the same reality he was probing with his lasers and his high-technology refrigeration coils."

8. Ideas from the trashcan

Outside my office at CERN, there is an e-waste trashcan. Hardware aficionados are often seen digging into these bins like sea gulls in search of a prize catch. Some of the kaput gizmos there are quite old and rare. Leaving aside their antique value, a piece of broken equipment is an invitation to read the designer's mind. What made them chose these components placed in that particular configuration? What were the limits of the technology of the day? Can it be repaired? Are there parts of the system that would still work perfectly well? What can you salvage from these tech fossils? As the legendary analog designer Jim Williams puts it, "The inside of a broken, but well-designed piece of test equipment is an extraordinarily effective classroom." The trashcan also inspires me to practice my French (because it sounds so much more dramatic): La poubelle est le meilleur endroit pour trouver quelque chose de valeur. Translation: The trashcan is the best place to find something of value.

I would close this essay with a caveat. In a discussion with Ashwin Khambadkone from National University of Singapore, he laments that academics are often bitten by the 'novel virus'. The fascination for the novel can lead away from the good. It is far more important to have good ideas than it is to have new ones.

What will you pass on?

As the river of time
Flows through your being
Pass on the largesse
To those downstream
And hold on to the gunk
For most will pass that too
Let the swirling currents
Break down the chunks of filth
Right from inside your hands
Until they’re rendered powerless
Your being will be clean again
Downstream will win, amen

The Power Electronics Resource List

Power electronics engineers are often looking for good ways to expand their knowledge of the field. In our conversations we start with reminding ourselves that the best learning comes from doing—experiments and simulation. Yet adding a dimension of learning from others' experiences definitely helps. Here I present a collected list of works that I have come to admire over the years (arranged in alphabetical order of author surname). As with most technical books, I have not read each one of them cover-to-cover, but have often referred parts of them and come back to the bench with lesser ignorance and some aha moments.  

Update: This list now incorporates additions based on suggestions from James Green, Iain Mosely, and Janamejaya Channegowda. Thank you!

Books

 

1. Basso 2008: 'Switch-Mode Power Supplies: SPICE Simulations and Practical Designs' by Christophe P. Basso  

 

2. Billings 2011: 'Switchmode Power Supply Handbook' by Keith Billings and Taylor Morey

 

3. Bossche 2005: 'Inductors and Transformers for Power Electronics' by Alex Van den Bossche and Vencislav Cekov Valchev

 

4. Brown 1990: 'Practical Switching Power Supply Design' by Marty Brown 

 

5. Brown 2001: 'Power Supply Cookbook' by Marty Brown 

 

6. Brown 2007: 'Power Sources and Supplies' by Marty Brown 

 

7. Dixon 2001: 'Unitrode Magnetics Design Handbook' by Lloyd Dixon 

 

8. Erickson 2001: 'Fundamentals of Power Electronics' by Robert Erickson  

 

9. Hart 2010: 'Power Electronics' by Daniel Hart  

 

10. Kazimierczuk 2008: 'Pulse-width Modulated DC–DC Power Converters' by Marian K. Kazimierczuk

 

11. Kazimierczuk 2014: 'High-Frequency Magnetic Components' by Marian K. Kazimierczuk 

 

12. Lenk 2005: 'Practical Design of Power Supplies' by Ron Lenk 

 

13. Maniktala 2006: 'Switching Power Supplies A to Z' by Sanjay Maniktala 

 

14. Mammano 2017: 'Fundamentals of Power Supply Design' by Robert Mammano 

 

15. McLyman 2004: 'Transformer and Inductor Design Handbook' by Colonel Wm. T. McLyman

 

16. Mohan 2007: 'Power Electronics: Converters, Applications, and Design' by Ned Mohan, Tore M. Undeland, and William P. Robbins 

 

17. Pressman 2009: 'Switching Power Supply Design' by Abraham Pressman, Keith Billings, and Taylor Morey 

 

18. Ramanarayanan 2008: 'Course Material on Switched Mode Power Conversion' by V Ramanarayanan 

 

19. Roberts 2017: 'DC/DC Book of Knowledge: Practical tips for the User' by Steve Roberts 

 

20. Roberts 2019: 'AC/DC Book of Knowledge: Practical tips for the User' by Steve Roberts 

 

21. Ruan 2014: 'Soft-Switching PWM Full-Bridge Converters: Topologies, Control, and Design' by Xinbo Ruan

 

22. Sandler 2014: 'Power Integrity: Measuring, Optimizing, and Troubleshooting Power Related Parameters in Electronics Systems' by Steve Sandler 

 

23. Sandler 2018: 'Switched-Mode Power Supply Simulation with SPICE' by Steve Sandler   

 

24. Umanand 1992: 'Design Of Magnetic Components For Switched Mode Power Converters' by L Umanand and S.P. Bhat

25. Umanand 2009: 'Power Electronics: Essentials & Applications' by L Umanand

 

 

Online resources

 

1. Bodo's Power Systems: https://www.bodospower.com/

 

2. How2Power: www.How2Power.com (Editor: David Morrison)

 

3. Iain Mosely Webinars: https://vimeo.com/powerguru

 

4. Monolithic Power Systems YouTube Channel: https://www.youtube.com/channel/UCqOx8jWRKEq4TpfcjCz0Isw

 

5. Ray Ridley Webinars: https://ridleyengineering.com/videos-e/
 

6. Texas Instruments 'Power Supply Design Seminar' (PSDS) archives (1983-present): https://ti.com/psds
 

7. Texas Instruments 'Power Tips' video series: https://training.ti.com/power-tips (Robert Kollman et al.)

 

8. Wurth Elektronik YouTube Channel: https://www.youtube.com/c/wuerthelektronik/videos

 

Ratings are overrated: From Goodhart to good heart

The above word cloud was generated using this online tool:
https://www.jasondavies.com/wordcloud/

Goodhart's law states, "When a metric becomes a target, it ceases to be a good metric." The moment one single parameter gains undue importance, many other equally (if not more) important aspects are ignored or side-lined. Any reasonably complex system is bound to have a dashboard of quantitative specifications and qualitative features which together ascertain proper functioning. To a lazy or overwhelmed observer, metrics provide a poor shortcut to circumvent the tedious process of judging the efficacy of a system. 

Once the players know that the observer has this parochial approach, they can choose to game their proposition to optimize for the single metric under the microscope, at the cost of everything else that matters. In many cases, this single metric is not just maximized, but also bloated beyond its true value to make it look larger-than-life, to make it stand out in the crowd. Once a sufficient number of players get into this habit, everybody else is pulled in just to stay afloat. We thus end up in a mad and meaningless rat race, caught in a vicious circle of megalomaniacal portrayals of ourselves, our work, and our creations. This is what I refer to as CV-lie-zation: The act of bloating up our CV to make us look much better than we are.

The overarching phenomenon of metric fever corrupts a multitude of processes, from marketing and buying of products to screening and selection of candidates in job interviews. A lot of our current systems are about running after these metrics, typically one in each domain. Grades in school, rankings of universities, citations of researchers, impact factor of academic journals, GDP or growth rate of an economy, profits of a business, TRP ratings of a television channel, followers on social media, customer ratings on online business platforms. 

All of these metrics have their place in informing assessments, but are extremely limited when each of them becomes the prima donna in their own domain. Let's resist the urge to oversimplify and embrace putting in the time and effort required to make important decisions. Ask for and evaluate much more than the metric.

Every once in a while...
- Ask a student with "poor grades" what excites them at school,
- Explore the tenth page of your Google online search results,
- Study that old paper with one citation,
- Read that unknown book that's rated one star on Amazon,
- Go to a hidden restaurant that's rated one star,
- Watch a forgotten movie that's rated one star on IMDb,

Valuable discoveries are made at places that no one is looking at. There are pearls hidden in the 'one star'. 

"If you only read the books that everyone else is reading, you can only think what everyone else is thinking." ~Haruki Murakami