Hi everybody, I am excited to start a new mini series of blog entries here that follows on the theme of “Science as Art” that other Global Research bloggers have covered in the past . You’ve seen images generated by our battery research, with our supercomputers, and learned about the beautiful images that can come from life sciences work.  However, over the next group of posts, I will be highlighting for you some of the work that can come out of materials characterization at GE Global Research.

As I’ve mentioned before, materials are at the core of almost every product and technology, making the materials characterization organization one of the most diverse groups within Global Research.  At the end of 2011, our “fun team” (yes, we do have fun at work) put together a really special project.  We prepared a 2012 calendar full of some of the most “beautiful” images that our group generated in 2011.  Calendars were provided to many of our colleagues, many of which fabricated the samples we analyzed; we also distributed them outside of our cafeteria, with the request for a donation to help support Habitat for Humanity and the Northeast Regional Food Bank.

For the next series of posts, I will be sharing the images from the calendar as well as some information about the materials in the images, the instrument which generated the images, and the team member that generated the image.  I hope you enjoy the photos, at the end of the series we will have a vote to see which is your favorite and we will select one of the voters and mail you one of our 2012 Materials Characterization calendars!

Our first image was generated by Ian Spinelli.  Ian tells us that his image (below) shows “a scanning electron micrograph of strengthening precipitates in a nickel-base superalloy.  Prior to imaging, a chemical etchant was used to remove the surrounding matrix.  The remaining particles are extremely small (the tiny spheres are only tens of nanometers in diameter).  They are formed by a process known as precipitation hardening, where the metal undergoes a heat-treatment in order to precipitate the particles from a supersaturated solid solution.  As a result, the chemistry and crystallography of the particles differ from the surrounding matrix, which makes them effective at impeding dislocation motion – the method by which metals deform under an applied load.  In summary, these precipitates give nickel-base superalloys the properties desirable for parts that are subjected to high temperatures and stresses, such as those found in gas turbines and jet engines.”

But you tell us… what do you see?

Recently, GE Reports posted a story on the GE Oil & Gas ORegen System that is being used on Canada’s Alliance Pipeline in both Alberta and British Columbia, Canada.  You may have seen the story if you follow the site OR if you follow the Managing Director of our technology center here in Munich, Carlos Haertel, on Twitter as he gave the Munich team a shout out!

ORegen is an ecomagination-certified product that traps waste heat generated by industrial machines and turns it into electricity.   Last year, GE partnered with NRGreen Power to bring this system to the Alliance Pipeline.  This collaboration generates 14 MW of cleaner electricity (enough to power 14,000 homes!), saves almost 3 million gallons of water annually, and is expected to eliminate 38,000 metric tons of CO2 per year.  All of which leads to $9 million/year revenue generation for our customer!

This is a project that has been worked upon and developed in part, right here at Global Research Munich.  Thomas Frey has blogged about the hidden source of energy that is waste heat recovery and my colleagues Matt Lehar and Christian Vogel continue to improve the ORegen technology today.  Learn more by visiting GE Reports.

Last month my manager asked my group if anyone would like to read the book Moneyball. I have been interested in baseball statistics since I was a child, so I volunteered to read the story of how Billy Beane, the Oakland A’s general manager, used the writings of Bill James, a writer and statistician, to help the A’s win games while keeping a low payroll. But, what amazed me is how I have lived my own personal Moneyball story.

In the early 1970’s I was a twelve year old boy who liked to play the game “Strat-O-Matic Baseball.” This game had cards for dozens of the best baseball players of all time. The cards were used to play a game that consisted of two parts. In the first part the historical players were drafted by the people playing the game. The second part consisted of using the drafted player’s cards to simulate a baseball game. Dice would be rolled for each batter and a table on each card would be used to determine what the result was for each at-bat. I wanted to know which players were the best ones to draft, so I used a simple formula to calculate how good each player was. The formula gave one point for a walk or single, two for a double, three for a triple, and four for a home run. This value was multiplied by the chance for that cell in the table being selected. Then all of those values were summed up to give the player a score. I did this for every card. It turned out that a player I was not too familiar with, Honus Wagner, had the highest score and a few others players my friends and I did not know well also had high scores.

I started picking these players in the draft phase and I started winning most of the games. This is not how I became a stats geek, this is just one of the things I did because I am a stats geek. So, in the 70’s, I acted similar to Bill James, the person who took the initiative to analyze statistics that no one else cared about and was able to generate knowledge and understanding about the subject of the data.

In 1985, I graduated college with a degree in computer science and started working on a software training program at GE Global Research. The training program had about a dozen people a year for three years, so there were about 36 of us in the program. The older people, who were on their later years of the program, had a softball team in the corporate league. A bunch of us in the new class wanted to play, but they only let one or two join the team. The rest of us were not good enough. So, the rest of us formed our own team.

We played the older team twice that season. The first time we played them they beat us pretty good. So, the second time we were to play they were boasting about how they were going to beat us again and even told us they were having a competition to see who on their team can hit the most home runs in their second win. We decided to put in a new rule for our team for this game called the “one strike rule.” This rule said that no one could swing at a ball until they had one strike. Most people played softball to hit home runs, and no one celebrated walks. But, the pitchers in this league had played all season knowing that people would swing at any pitches, so they would almost never threw strikes. We walked and got deep into counts which forced the pitcher to try and throw strikes, just like the Oakland A’s did in 2002. And we beat the team that said none of us were good enough to play on their team. Then a few years later our team, with a few recruiting additions, won the league championship, partially because of the plate discipline we obtained from situational use of rules like the “one strike rule.”

One of the roles I had on the team was “stats man.” I would keep the book on the game (recording every at bat). Then, the day after the game, I would send out an e-mail with the on-base-percentage, average, and slugging for everyone in that game along with season totals. So, in the 80’s, we acted similar to Billy Beane, the baseball general manager who used statistics and analytics to help his team win.

In the 1990’s, I started teaching a class on applied intelligent reasoning systems for the computer science department of RPI as an adjunct professor in addition to working at GE. I wanted to create an in-class exercise that would be interesting for the students. At this same time it was baseball season and a young Derek Jeter was up for a new contract next season. I thought it would be fun to have the class determine how much the Yankees should pay Jeter. I was able to obtain data on players batting statistics and salaries from the espn.com website. Using this we selected the best stats for comparing players. The two stats we determined were the best were on-base-percentage and slugging.

Interestingly, the next year ESPN added a new stat to their lists. The stat they added is OPS, short for on-base-percentage plus slugging. OPS is now a single stat many people use to compare players. The class appraised Derek Jeter’s salary by calculating his OPS, finding three other players with similar OPS who have recently signed contracts (with some preference for players at the same position, same age, and from cities with the same market size), and using these three salaries to calculate what Jeter should be paid. The three similar players had their salaries adapted higher if they had a lower OPS or lower if they had a higher OPS. There were a few other adaptations made. Finally, the average of the three adapted salaries was calculated at $5M. That year the Yankees decided to pay Jeter $5M– the same as our analysis.

I am still using this exercise as part of teaching the Edison A class on computer algorithms. One of our projects at GE Global Research used this same appraisal technique to automate the appraisal of residential property. The papers and patents on the automated residential property system have been sited over a hundred times. So, in the 90’s we went beyond what was done in the book Moneyball and created intelligent systems based on the analytics!

As a company that makes so many things that spin in a circular motion … think jet engines, wind turbines and even MRI scanners … we’re big fans of Pi! Pi, that magical measurement defining the ratio of the circumference of the circle to its diameter, is important to designing these products and so many more that we manufacture.

To us, Pi is much more than a number.

For wind turbines, it can help us analyze wind blade speeds at their tips as we look for ways to improve wind capture and design the other rotating wind turbine parts. 

For MRI scanners, PI helps us design to achieve the best medical images.

Pi is also important for 3-D modeling and printing, which is an area where GE researchers are intensifying their efforts. 3-D printing, an area of additive manufacturing, is providing new manufacturing freedom that was not possible with conventional machining processes. Being able to take precise measurements is vital to this emerging manufacturing technique.

Happy Pi Day!