June 2009 Archives

With all the hoopla around alternative-propulsion vehicles (e.g., electric cars and hybrids), not too much gets into the mass media about the efficiency of rail transport. Those of us who've lived in the Far West don't need to be reminded of railroad transportation. Running individual truckloads of goods along highways one-by-one seems kinda silly next to mile-long freight trains.

BNSF Railway Company recently announced two free tools for shippers and carriers to be able to directly compare the cost and transit times of intermodal service to the highway alternative. The company says that shipping by intermodal rather than solely by highway provides important environmental, safety and security benefits.

In the late 1949s, when then Gen. Dwight Eisenhower became impressed with the German Autobahn system, rail cars had to be individually loaded and unloaded at each end of any transportation run. So, a shipment of barbell weights cast in China would have been loaded on a ship, sailed across the Pacific Ocean, unloaded in, say, San Pedro, Calif., then unpacked from the freighter and repacked into boxcars. After making the rail trip to, say, Erie, Penn., they'd have been unpacked from the train, and repacked into freight trucks for delivery to wherever they were finally to go. All that packing and unpacking took a lot of time and labor.

A few years later, when elected President of the United States, Eisenhower took the opportunity to reproduce the Autobahn system on a grand scale in the Interstate Highway system.

Figure 1: Movement of a truck through the atmosphere builds high air pressure ahead and pulls low pressure behind. Both effects create retarding forces on the truck. This effect is called induced drag. (Click to expand)

Since then, however, we've developed the intermodal transportation system. With intermodal, everything destined for that Erie location from that China starting point would be packed in ISO shipping containers, which are exactly the size and shape of the boxy trailers that long-haul trucks pull, sans the wheels. At the Chinese port, these containers would be tightly packed into cavernous holds of dry-shipping freighters headed for the U.S. port. Once there, the containers would be lifted out of the holds, and stacked two-to-three high on railroad flatcars made for the purpose. The flatcars would be made up into trains for the transcontinental passage. In Erie, each container would be lifted from the rail cars onto an individual truck for final delivery. That makes the system time and labor efficient.

I won't go into the safety and security benefits, as they are not basic technology issues. The environmental aspects, however, stem from basic physics and engineering. Specifically, high-speed rail transport can be (should be) more fuel efficient than highway transport.

There are two main forces that cause vehicles to burn fuel at high speeds: aerodynamic drag and rolling friction. I'll start with rolling friction.

The main cause of rolling friction is deformation of the wheels as they generate reaction forces to support the vehicle's gross weight. These deformations convert kinetic energy of the rolling wheel to heat. The more deformation and the faster the wheel rotates, the greater the heat. That is why truck tires are so large in diameter (fewer revolutions per mile) and why they run at very high pressures (less deformation). Rail cars, on the other hand, have steel tires, rather than pneumatically supported rubber tires, so they hardly deform at all, even when carrying the enormous gross weight of a fully loaded rail car. Thus, rolling friction per unit weight in railroad transport is a fraction of that for highway transport.

Aerodynamic drag arises from the need to elbow air out from in front of the vehicle, then suck it back to fill the hole in the atmosphere after it passes. Figure 1 shows how high pressure builds up in front of a highway freight truck, and low pressure forms behind it. These high and low pressure regions create forces that hold the truck back - aerodynamic drag.

Figure 2: Running two trucks in tandem dangerously close together neutralizes the low pressure region behind the first truck and the high pressure region in front of the second, reducing the aerodynamic drag by nearly half. (Click to expand)

Drag forces increase as the square of the vehicle's speed, so they rapidly become the dominant energy-loss mechanism.

Truckers soon learned that the best way to reduce aerodynamic drag is to run trucks nose-to-tail close enough so the high-pressure zone in front of the following truck overlaps the low-pressure zone behind the truck ahead. As Figure 2 shows, the two pressure zones cancel each other out, effectively cutting the net aerodynamic drag in half. This so-called drafting technique has been used for decades by truck "convoys" to lower operating expenses by saving fuel. The more trucks in the convoy, the more fuel saved.

Figure 3: Because they are physically coupled together, railroad cars can run safely with very little spacing between them, providing huge aerodynamic drag advantages. This drafting phenomenon effectively neutralizes induced drag for all but the lead and last cars. It does not, however, reduce viscous drag caused by sliding of air past the cars tops and sides. (Click to expand)

The same effect improves aerodynamic efficiency of railroad transport, except that the trains are very much longer and the cars can be safely run very much closer.

These two effects boosting overland transport efficiency makes maximizing use of rail transport good energy policy. What we, as ordinary citizens, can do is raise the volume of voices calling for increased use of rail transport as part of energy policy. Since the same phenomena apply to passenger transport compared to individual cars, we should also clamor for upgrading commuter rail as an alternative to commuting via cars.

Nearly every technophile on Earth has seen Star Wars medical droids subbing for human physicians, surgeons, and other medical professionals. Unlike most technological marvels portrayed by Hollywood as existing sometime in the far future, such robots aren't that far from reality. A case in point is GeckoSystems Intl. Corp.'s CareBot robotic elder-care system, which graduated to nurses' aid status with the addition of a miniaturized, solid state onboard blood pressure and pulse rate monitor.

"We believe that the incorporation of an onboard blood pressure/pulse rate monitoring system for our CareBots will further enhance their cost effective, utilitarian capabilities. Our CareBot's ability to automatically follow and verbally remind a designated care receiver at predetermined dates and times that their blood pressure/pulse rate needs to be checked by this onboard, integrated monitoring system will enable a higher level of safety, security and cost savings for those at home and in nursing homes, assisted care facilities, hospitals, etc.," observed Martin Spencer, President/CEO of GeckoSystems.

The company says CareBot is a multitasking personal robot incorporating advanced, proprietary AI engines. Given the CareBot's network connectivity and Internet accessibility, alerts of vital signs and other various healthcare events outside of normal range can be quickly sent by telephone, instant or text messaging, and/or email.

GeckoSystems uses sensor fusion extensively for actionable situation awareness in their complete multitasking personal robot, the CareBot. Their mobile robot's hardware and software architecture is designed to be expandable and upgradeable such that many years of cost effective usage can be readily achieved.

The primary market for this product is the family for use in eldercare, care for the chronically ill, and childcare. The primary distribution channel for this new home appliance is the thousands of independent personal computer retailers in the U.S.

Spencer suggests thinking of it as a new type of labor saving, time management automatic home appliance. The unit decreases the difficulty and stress for the caregiver who needs to watch over family members most, if not much, of the time day in and day out due to concerns about their well being, safety, and security. Not infrequently, the primary caregiver has a 24 hour, 7 days a week responsibility. There is concern that medication will be missed or the care receiver have an accident requiring immediate assistance. And the care receiver may be very resistant to a "stranger" coming in to her home and "running things" in the care giver's absence.

Spencer points out that the CareBot is a new kind of companion that always stays close to the care receiver, enabling family and friends to care for them from afar. It tells them jokes, retells family anecdotes, reminds them to take medication, reminds them that family is coming over soon (or not at all), recites Bible verses, plays favorite songs and/or other music. It alerts them when unexpected visitors, or intruders are present. It notifies designated caregivers when a potentially harmful event has occurred, such as a fall, fire in the home, or simply been not found by the CareBot for too long. It responds to calls for help and notifies those that the caregiver determined should be immediately notified when any predetermined adverse event occurs.

The family can customize the personality of the CareBot, modulating the voice's cadence to be fast or slow. The intonation can be breathy, or abrupt. The voice's volume can range from very loud to very soft. The response phrases from the CareBot for recognized words and phrases can be colloquial and/or unique to the family's own heritage. The personality can range from brassy to timid depending on how the caregiver, and others appropriate, chooses it to be.

Addition of medical-condition monitoring technology is a landmark for the robotic care system, upgrading its functionality from strictly social interaction as a companion (no mean feat itself!) to managed-care activity that may be beyond the capabilities of an untrained human caregiver.

Robots have always been able to perform complex ballets of motion with varying degrees of precision. Early examples, dating back to ancient Greek "miracle" machines, tended to be halting and jerky in their mechanically driven movements. Computerized motion control allowed them to reach super-human levels of precision, with smooth, flowing trajectories a ballerina would envy.

The current generation of robots are even able to adjust their routines for variations in positions of objects in the environment. For example, I saw several demonstrations at the 2009 International Robots, Vision & Motion Control Show and Conference, (R&V) ongoing at the Steven, Convention Center in Rosemont, IL, of robots able to reach into a bin containing many interchangeable parts piled randomly, select the unit easiest to extract from the bin, figure out the most convenient way to grab the part, and pick it up. Issues like units lying atop one another so that their images overlap in the robot's field of vision, which just a few years ago were application breakers, are no longer an issue.

These robots, however, are only able to perform because a human wrote a detailed instruction program for them, which specified each motion in minute detail. These robots do not think or plan at any sophisticated level. They act as a human engineer has programmed them ot act.

Figure 1: Lipson's self-learning robot has to experiment to develop its own self image.

That's not how Earth's most sophisticated machines -- living animals -- operate. I did not come with an instruction manual. Neither did you. Neither did your family dog, or the fleas crawling on his back. Amoebae, which rely on hundreds of flailing scilia to get from where they are to where they want to go to find their next meal. A newborn giraffe has to figure out things like how many joints it has and how to use its muscles to stand up, and it has only until the next lion attack to solve the problem.

In an effort to learn how newborn animals do it, Cornell University Associate Professor Hod Lipson is studying what he calls "artificial life." That is, self-organizing systems whose only goal is to get up and move about on their own.

I ran into Dr. Lipson at his booth tucked into a corner booth in the Emerging Robotics Pavilion at R&V, next to headliner Toyota's Partner Robot, which wowed audiences by playing trumpet (quite well, thank you very much). Lipson quietly pointed out that his robot, a four limbed star-shaped entity that looks like a cross between a starfish and a Lego set, is at the opposite extreme from Toyota's robot.

Partner is smooth, polished, and expressive, with fluid movements and a sophisticated reportoire of behaviors, which it received directly from human programmers who choreographed every move.

Lipson's unnamed entity, on the other hand, exhibits all the style and grace of a young ox. That's because it's on its own. All it started with was the knowledge that it had eight motors and two tilt sensors, and it wanted to get up and move. A video on display at the booth documents the stages it went through in its quest for self-image and locomotion. Starting with random motions it used to find what actuator motions caused what changes in tilt-sensor outputs, developed a self-image that accurately reflects its body's topology: four limbs consisting of two segments each with actuators controlling each joint, all connected to a central platform carrying the tilt sensors. Then it experimented with coordinated motions in an attempt to find gaits that would allow it to move about in various directions. Finally, it crawled off into the sunset (represented by the edge of the table).

Finally, Lipson simulated an injury by dismounting part of one limb. The movement it had learned no longer worked, so the robot had to, through trial and error, find another gait that would again allow it to walk off into the sunset, albeit with a decided limp.

Lipson's work obviously is helping life scientists understand what information an organism needs to have coded into its DNA to live and thrive. In addition, it may help future robotics engineers develop robots that can learn on their own, instead of needing detailed programming for every movement they make.

As with so many terms bandied about in mass media, "Smart Grid" is a cutesy umbrella term that allows politicians, analysts, and newscasters to vaguely refer to a collection of technologies that neither they nor their audiences fully comprehend, with advantages that are easily stated, and of uncertain measurability.

While that sounds pretty negative, let me point out that nothing in the above paragraph says anything against the technologies themselves, or their value, but merely pans vague marketspeak terms in general, and the folks who rely on them for ... anything.

Smart grids are part of a general technology trend toward incorporating embedded microcontrollers and data-communication capabilities into all sorts of previously existing devices. For those unfamiliar with them, a "microcontroller" is an integrated circuit that includes a microprocessor and peripheral circuits that allow the microprocessor to sense conditions and events in the external world (data acquisition) and put out signals to drive actuators in the external world (control).

Perhaps the first "smart" devices were automobile engines, which came under microprocessor control during the late 1970s, long before the term "smart xxx" became current. Such engine control modules (ECMs) sensed such variables as outside air temperature and throttle position, and used that information to control such parameters as fuel/air ratio and spark timing. Later, ECMs gained the ability to communicate with additional embedded microcontrollers managing such functions as anti-lock braking systems (ABS) and alarm systems. Modern automobiles now contain dozens of networked microcontrollers operating nearly all functions.

Today, most significant appliances operate under guidance of microcontrollers. Microwave ovens, dishwashers, clothes dryers, televisions, and home thermostats are familiar examples. The extent to which manufacturing operations rely on "smart" technology is even more profound.

Electricity generation and distribution networks, however, are far behind other industries in incorporating smart technology. That is the impetus behind all of the noise and fury about "Smart Grids" in the media.

To be fair, there are significant barriers to incorporating smart technology into electric-power infrastructure. Most significantly, it is imperative to keep the system operating reliably while applying new technology to it. Second, the cost of upgrading existing equipment that was never intended to be part of a computer-integrated system is, shall we say, large. There are many additional issues to be considered when making the move to smart utility grids.

The motivation to incorporate computer control and networking technology into the electric power system is not just to make it more "modern." The concept avoids Scheiber's Rule (Just because you can doesn't mean you should.) by solving a number of present and future problems arising from electric-utility development trends.

The first issue is the fact that the present distribution grid developed from early systems where a single generating plant distributed power to an isolated netword of loads. That placed the responsibility for maintaining voltage, frequency, and phase of the provided electricity squarely on one generating facility. Such installations are amenable to simple closed-loop control.

Later, but still quite some time ago, outputs from multiple generating plants were combined to supply power to the user network. That created the issue of coordinating the output levels and phases of the sources. At least, the sources on a given network were controlled by a common authority capable of centrally guiding the generators via more complex closed-loop control.

Problems became serious when power-distribution networks were interconnected to allow power sharing between sources operated by separate authorities. This makes simple reactive closed-loop control problematic. When you have multiple agents independently providing control inputs in response to observed conditions, the system becomes chaotic. This is not a slam on the engineers who designed and operated the system. It's a fact of life dictated by mathematics. Voltage variations, unpredictable frequency and phase shifts, and seemingly random catastrophic failures ensue.

Happily, all the folks on the supply side of the system were highly intelligent professionals who realized that the only solution was to co-operate their power-generation controls. We'll call it meta-control, where individual operators don't blindly react to every movement of the controlled system, which is what drives the system into chaotic behavior. Instead, when they observe a departure from nominal status, they first communicate among themselves, and devise a coordinated response that brings the entire system back toward nominal.

You can do that when there are relatively few operators. As the number of operators grows, the time needed to communicate and devise a coordinated strategy becomes longer, while the frequency and severity of divergences become more severe.

In the past, the economics of power-generation have favored large generating stations because they can be made more efficient. Costs for fossil fuels and nuclear power scale more slowly than generating plants' output. Emerging energy sources, such as photoelectric and wind power, have been billed as "free energy sources," although they are nothing of the kind, so power-plant efficiency figures less in the installation decision. Thus, we expect to see many more smaller plants. With more small plants, the number of sources that need to be coordinated will rise dramatically, and system-control cost and difficulty will increase.

The assumption is that increased deployment of smart-grid technology will make it possible to maintain system control in the face of increased chaos. High-speed data sharing is to improve coordination while expanded computer automation improves the speed and quality of meta-control decision making.

According to Wikipedia, support for smart grids became federal policy with passage of the Energy Independence and Security Act of 2007. The law, Title13, set out $100 million per fiscal year in funding for fiscal years 2008-2012, established a matching program for states, utilities and consumers to build smart grid capabilities, and created a Grid Modernization Commission to assess the benefits of demand response, and recommend protocol standards.

The Act directs the National Institute of Standards and Technology (NIST) to coordinate the development of smart grid standards, which the Federal Energy Regulatory Commission (FERC) would then promulgate through official rulemakings. Smart grids received further support with the passage of the American Recovery and Reinvestment Act of 2009, which set aside $11 billion for the creation of a smart grid.

Progress has been swift, as it needs to be. Federal Energy Regulatory Commission (FERC) issued a proposed policy statement and action plan on 19 March 2009 for standards governing the development of a smart grid. However, FERC noted that the electric industry started moving ahead with smart grid technologies prior to these government initiatives. The Commission is proposing to establish some general principles that the smart grid standards should follow.

We have known for some years that the trend was toward more numerous smaller power plants. The handwriting has been on the wall since the introduction of a feed-in tariff (FIT) system in 1978. A feed-in tariff is an incentive structure to encourage the adoption of renewable energy through government legislation. The regional or national electricity utilities are obligated to buy renewable electricity (electricity generated from renewable sources, such as solar photovoltaics, wind power, biomass, hydropower and geothermal power) at above-market rates set by the government. The higher price helps overcome the cost disadvantages of renewable energy sources. The rate may differ among various forms of power generation.

FIT means that any Tom, Dick, and Harriett with access to enough cash can set up a generating station, then sell the power to utilities, which are obliged to buy it. This model works well for facilities, such as hospitals and certain manufacturing operations, that need to maintain back-up power generation plants in the event of power failure. Most of the time these generators stand idle. FIT allows their owners to defray some of their cost by running them during peak periods, when demand may exceed fixed-power plant capacity and electricity costs (and FIT repayments) are largest.

The unintended consequence, of course, was a more chaotic electricity environment. Specifically, since a hallmark of chaotic systems is scale invariance, departures from nominal expanded to higher spectral frequencies with smaller amplitude signals (amplitude varies inversely with frequency. While these departures are smaller, their higher frequency translates into the need for faster response. Utilities began experimenting with smart-grid technology in hope of reigning in chaos over a much larger bandwidth.

ADDITIONAL RESOURCES:

U.S. Department of Energy Smart Grid

IBM Smart Grid

American Superconductor Smart Grid: It's More than you Think