The Hyperloop: A Push for the Alternative is Real

Catalyst Volume 7

The drive down CA I-5 from San Francisco to L.A. takes six hours. That’s six grueling hours of negotiating the horrors of urban traffic, trying to stay awake over vast stretches of monotonous Central California farmland, and watching your gas tank guzzle your hard-earned dollars. A plane ride? A nonstop flight would take only an hour and a half, but that’s without considering the time it takes to pass through security or the price of checking in any luggage. In an age where a thought can be sent half-way around the world in mere seconds, this commute is one emphatic no-no to the time-strapped millennial American. Imagine an alternative. What if there was a way to travel this same distance in just half an hour—and for only twenty dollars a ride?

Say hello to the hyperloop.

The summer of 2013, entrepreneur Elon Musk suggested an alternative method of travel: the “Hyperloop,” a proposed form of high-speed transportation with the potential to travel up to 760 mph.¹ To put things into perspective, Japanese maglev (magnetic levitation) trains have maxed out at 361 mph, and the commercial Boeing 747 plane travels at an average of 570 mph.² The speed of sound is 767 mph. On top of it all, while the California High-Speed Rail Project is currently asking for over $68 billion dollars, Elon Musk estimates that constructing the Hyperloop would only cost $6 billion.

Subsonic speeds at one-tenth of the California High-Speed Rail Project—this audacious claim is not a first for Elon Musk, the man behind the Hyperloop. Take a look at his distinctive accomplishments as CEO and founder of the companies PayPal, Space X, and Tesla: today, the online payment service PayPal is ubiquitous; Space X is in the works of delivering its third interstellar manned vehicle prototype; and Tesla’s electric car stocks trade at over $170 a share. Clearly, Elon Musk has been the impetus behind phenomenal and successful ideas. Still, his history of successful ideas does not guarantee that the Hyperloop would be a successful project. Furthermore, Elon Musk has publicly announced that he will not be tackling the Hyperloop. However, he has released a 57 page report detailing his ideas, which are freely available for anyone to view online. Based on this information, let’s take a look at how this project might be able to work.

A brief technical breakdown

If the Hyperloop sounds like something straight out of science fiction, you wouldn’t be far from the truth. Science fiction authors have written about something that sounds very much like the Hyperloop since the 1900s. In addition to its basic design, the proposed technology for the Hyperloop has also been around for many years.

The technology behind the Hyperloop is the implementation of pneumatic action, which relies on the compression of air to induce movement. Starting in the mid-to-late 19th century, numerous major cities in the U.S. and other countries relied heavily on massive underground networks of pneumatic tubes to send mail. Although this technology has been replaced by email, pneumatic action is still at work in post offices and hospitals today. Pneumatic action is also integral to air hockey tables, which utilizes air to reduce friction. In fact, Elon Musk described the Hyperloop as a cross between a railgun, a Concorde, and an air hockey table.¹

More precisely put, the Hyperloop is a closed-tube transportation system that provides uninterrupted traffic in both directions, perhaps akin to the energy-efficient, tiny maglev pods inside of elevated tubes. This project differs from existing infrastructure developments in three predominant ways by combining a partially evacuated tube with pressure-regulating pods on elevated concrete pylons.¹ Though these features are not new, the combination of these attributes is indeed novel.

To carry passengers inside the tubes, two different distinct pod systems have been proposed: The first is an all-passenger version carrying 28 seats. The second, larger system would carry three automobiles and their passengers. No matter which system is utilized, each pod would have pressurized cabins containing backup air supply and oxygen masks in case of emergency, much like an airplane.¹

The pods would travel very fast—up to sub-sonic speeds—due to a nearly frictionless journey.¹ The steel tubes play an important role by containing a partial vacuum, one ideally one-sixth of the air pressure on Mars. Rather than relying solely on electromagnetism for the entirety of the trip, friction would also be greatly reduced thanks to electric compressor fans attached to the traveling pods.

The electric compressor fans can help overcome the Kantrowitz Limit, a limitation on mass flow related to the size ratio between pod and tube. If the space between a pod and the interior of the tube is too small, then the Hyperloop system will behave like a syringe, where the pod will push the entire column of air in the tube. The compressor fans address this problem by pushing high pressure air from the front to the rear to create a cushion of air underneath the pod.¹

To lower energy costs on the environment, the bulk of the system would be powered by a solar generator. This solar energy would be split between charging the pod batteries and the rest of the system. Musk’s plan would rev up the pods from their stations using magnetic linear accelerators resembling railguns;¹ once in the main travel tubes, the pods would be given periodic boosts by external linear electric motors similar to those in the Tesla Model S.

Finally, in order to lower the cost of construction, the partially-evacuated tubes would be elevated on concrete pylons. This type of system mitigates damage caused by earthquakes and reduces maintenance costs.¹ Additionally, elevation would allow the Hyperloop to travel without interruption, bypassing ground traffic, farmland, and wildlife. By building on pylons, the system could also follow the California I-5 highway, reducing the need for expensive land acquisition suits.

Again, these suggested attributes for the Hyperloop already exist in one form or another, but they have not yet been combined in such a manner. For instance, much of the Federal Highway System uses concrete pylons to elevate interwoven freeways. The subways of New York City and Chicago are enclosed systems, but they do not run on cushions of airs. Maglevs run on a cushion of air, but they are not enclosed.

So what does this mean?

All in all, the scientific community generally seems to agree that the Hyperloop is technically feasible. However, Elon Musk’s proposal does come with many caveats. The most significant problems seem to stem from economic and political concerns. Critics have claimed that Elon Musk’s idea is overtly conceptual and utilizes unrealistic cost estimations. The estimations are particularly contentious because the Hyperloop proposal is reminiscent of the California High-Speed Rail, a project with initial estimates that were much lower than the current budget; in 2008, state voters had approved $9.91 billion dollars for the California High-Speed Rail Project. By April 2012, High-Speed Rail estimates had reached $91.4 billion, nearly ten times original estimates, before public outcry triggered a budget revision.

However, the Hyperloop manages to avert many of the same issues plaguing the California rail project. One benefit of building on concrete pylons is a reduction in land acquisition, currently the most expensive and politically contentious issue affecting transportation projects. Another benefit of the project is an independence from federal funding. Without a reliance on bond measures or public coffers, the Hyperloop would not need to build in potentially unprofitable areas to keep policymakers satisfied. Free from political strings, it could focus on creating a profitable, sustainable venture.

Regardless of the advantages, it seems unlikely that construction on the Hyperloop following the proposed route between San Francisco and Los Angeles would begin during the construction of the California High-Speed Rail. This is unfortunate, as a route between L.A. and San Francisco guarantees a customer base. Furthermore, city pairs that are 120 to 900 miles apart are ideal for rail or similar transportation, as this distance is too far to comfortably travel by car and yet too close to efficiently travel by plane.⁷ L.A. and San Francisco fit perfectly in that niche.

Elon Musk has not made any plans to develop the Hyperloop, causing many to believe that the entrepreneur may be using the Hyperloop as an elaborate red herring. One critic has pointed out that the construction of the California High-Speed Rail would directly compete with automobiles in Silicon Valley, one of Tesla’s target market demographics. Could Musk trying to derail the already tenuous rail initiative in order to reduce economic competition? Interestingly, after the official Hyperloop press release, local politicians in the Silicon Valley area lobbied to prevent the California High-Speed Rail from developing in their respective constituent zones. Was this mere coincidence?

Regardless, the most valuable attribute of the Hyperloop project is its status as an open-source engineering project open to privatization. Using the same basic principles, one man has even built a working miniature prototype with the capacity to hover on a cushion of air. Autodesk, a 3D design software, has produced realistic, promotional renderings of the Hyperloop. ET3 is a company that is working on similar evacuated vacuum tube designs.³ In this day and age, crowdfunding platforms like Kickstarter and microfunding organizations like Kiva could potentially be used to financially support the construction of the Hyperloop. In the Netherlands, the company Windcentrale raised over 1.3 million Euros in only thirteen hours from 1700 Dutch households eager for a local wind turbine.⁵ Money for the Hyperloop could be similarly raised.

Whatever the case, one thing is clear: even if the Hyperloop remains a conceptual project, the idea has opened up an important dialogue regarding the future of mass transportation in America. It has bought attention to the inefficiencies of the California High-Speed Rail Project, and it has renewed interest in technological advances for public transportation. The Hyperloop might also lead the U.S. towards privatized or crowdfunded infrastructure. Perhaps most importantly, the buzz around the Hyperloop has reaffirmed the need for alternative transportation.

References

1. Musk, Elon. Hyperloop Alpha. https://www.teslamotors.com/sites/default/files/blog_images/hyperloop-alpha.pdf (accessed Oct 20, 2013).
2. 747 Family. http://www.boeing.com/boeing/commercial/747family/background.page (accessed Nov 4, 2013).
3. “The Evacuated Tube Transport Technology Network.” ET3, 24 Oct. 2013. <http://et3.net/>.
4. United States Bureau. The California Energy Commission. California Gasoline Statistics & Data. Energy Almanac. Web. 30 Oct. 2013. <http://energyalmanac.ca.gov/gasoline/>
5. “Dutch Wind Turbine Purchase Sets World Crowdfunding Record.” Renewable Energy World. Ed. Tildy Bayar. 24 Sept. 2013. Web. <http://www.renewableenergyworld.com/rea/news/article/2013/09/dutch-wind-turbine-purchase-sets-world-crowdfunding-record>.
6. “2012 Annual Urban Mobility Report.” Urban Mobility Information. Texas A&M Transportation Institute. Web. 11 Oct. 2013. <http://mobility.tamu.edu/ums/>.
7. “Competitive Interaction Between Airports, Airlines, and High Speed Rail.” Joint Transport Research Centre. Paris. Discussion Papers.7 (2009): 20. 8 Dec. 2013.
8. Central Intelligence Agency. The World Factbook. n.d. Web. 18 Nov. 2013. <https://www.cia.gov/library/publications/the­world­factbook/>.
9. Levinson, David. “Density and Dispersion: the Co­development of Land use and Rail.” London Journal of Economic Geography, 8 (1), 55­77. 10.1093/jeg/lbm038. 2007.

Electronic Foil: Blurring the Interface Between Computers and Bodies

Catalyst Volume 7

What if computer technology was to integrate within our bodies? Think of all the possibilities. We could install built-in health monitoring systems as well as technological communication devices. These seemingly far-off advances are exactly what material scientists are currently trying to accomplish.

In a landmark development, researchers have successfully designed electronic interfaces that can be fully implemented into the human body. Dubbed "electronic foils," this invention allows circuits to conform to any surface, giving them the unique ability to adapt to a moving body part. These electronics—which can be stretched, bent, and crumpled—may someday become as common as plastic wrap.¹ Wearable technologies have potential applications ranging from medical diagnostics and wound healing to video game control. Electronic skin has been shown to monitor patients' health measurements as effectively as conventional, state-of-the-art electrodes that require bulky pads, straps, and irritating adhesive gels.²

Traditional electronics are hard and unyielding, typically making them unable to serve in biological applications.¹ However, developments in materials science are transforming the way we think about electronics. Dr. John Rogers, an engineer at the University of Illinois at Urbana-Champaign, studies the characteristics and applications of "soft materials,” or those without physical restraints. Scientists have engineered “transient electronics,” a new class of electronics that can degrade completely after carrying out a designated task. The potential applications of this technology are impressive. For example, Rogers and his team successfully embedded heat-sensitive, transient circuits into surgical wounds of mice to fight infection. These devices were then absorbed into surrounding tissue after a threshold exposure to biological fluids.³

The secret to transient electronics’ disappearing act lies in their material composition. The circuits implanted in the mice were crafted from sheets of porous silicon and magnesium electrodes packaged in enzymatically-degradable silk. Its solubility in water is programmable thorough the addition of magnesium oxide to create a crystalline structure. By tweaking the framework, researchers can control how quickly the engineered silk dissolves, be it over a matter of seconds or several years.³ Scientists foresee applications in which these devices are placed on a wound or inside the body immediately after surgery, so they can integrate and wirelessly transmit information,¹ such as heart rate, blood pressure, and blood oxygen saturation levels.³

Using Roger’s work, Martin Kaltenbrunner, an engineer at the University of Tokyo, discovered how to further expand the applications of flexible electronics and demonstrated a number of uses for their virtually unbreakable circuits. For example, the researchers built a thin transistor incorporated into a tactile sensor that conforms to uneven surfaces. By placing such a device on the roof of a person's mouth, paralyzed patients could give yes or no answers by touching different spots of the sensor. Touch sensitivity could also help those with artificial limbs to gain feeling. Kaltenbrunner’s team also built a mini temperature sensor that adheres to a person's finger. In the future, this simple sensor could be implemented in an imperceptible adhesive bandage for health monitoring.³

The lines between human and machine are becoming blurred. As frightening as seems, the impact of electronic foils on biomedical devices is a positive one. Health monitoring will become commonplace, and the skin can be used as a signal transductor. With the onset of these human-integrable electronics, the field of biomedicine will be completely revolutionized.

References

1. Kaltenbrunner, M. et al. Nature 2013, 499, 458-463.
2. Kim, D. et al. Science 2011, 333, 838-843.
3. Hwang, S. et al. Science 2012, 337, 1640-1644. 

Hands-free driving: A Roadmap to the Future

Catalyst Volume 7

The simple act of driving can be an unproductive, dangerous, and time consuming activity, one that can be solved through the installation of autonomous technology within vehicles. This technology is considered to be among the most crucial breakthroughs in human travel that is being developed today; it is believed to have the capacity to create an improved and efficient driving experience by limiting fuel consumption, decreasing traffic congestion, and reducing wasted time during road trips.

One of the driving forces behind the creation of autonomous vehicles is safety. Autonomous technology promises safer travel compared to human-operated vehicles, as the cars are equipped with laser and video detection systems to control the car's speed and steering mechanisms while avoiding obstacles in the roadway. This blend of autonomous technologies promises to make driving 99% safer while also allowing the travelers to focus on other activities.¹

These cars must detect and make rapid decisions to avoid objects in the roadway; the simple act of crossing an intersection requires the robotic cars to account for the inertias, right-of-way, and velocity of approaching vehicles.² A major problem facing autonomous vehicles is the idea of real-time communication. As humans correspond face-to-face, these autonomous cars need to interact in real-time, allowing the cars to work together safely. However, this type of communication is unpredictable and extremely hard to maintain.³ Autonomous technology presents near endless benefits to automobile commuters; however, this technology faces not only current mechanical and software problems but also major legal and social issues. This technology needs to be perfected in every way possible before being released into city streets. Through my review of the autonomous technology within these computer-driven cars, I will explore the type of technology that operates these cars, how it operates the vehicle, the benefits created from this technology, and any possible legal and social concerns that arise from their use.

Developing Technologies: Seeing, Thinking, Steering

The ability for autonomous cars to see and judge risks in the roadway is vital to safe operation of the vehicle. An outstanding prototype of autonomous technology was created in 2007 by the Stanford Racing Team. Their robotic car Stanley, which won the DARPA Grand Challenge, operated solely on a software system that processed and converted visual data into appropriate driving commands.⁴ This software system uses an onboard sensors including lasers, cameras, and radar instruments to gather outside information from the road, allowing the robotic vehicle to observe and judge the approaching roadway;⁴ these sensors are placed on top of the vehicle. The combination of lasers and cameras allows for increased detection of obstacles by allowing both short and long range detection, respectively.⁴ As the cameras receive the long range images, the lasers allow the vehicle to detect the dimension of approaching objects that could harm the vehicle. Detection of hazardous obstacles is one of the easier aspects of autonomous driving; split second decision-making based on the detection system is harder to accomplish. An autonomous vehicle must use the information from the detection systems to determine if the road surface is safe for driving. Measuring the dimensions of detected objects allows the car to determine if they are true obstacles, such as roadway debris, or non-obstacles, such as grass and gravel. The researchers who helped build Stanley stated that the robot had trouble determining the difference between tall grass and rocks, which poses obvious difficulties in application.⁴ In addition to obstacle recognition software, autonomous vehicles require extensive algorithms to accomplish and maintain velocity, steering, acceleration, and braking—functions all controlled by the same system of detection and decision making.

Dynamically Guided Routes

Route guidance is core to autonomous vehicle technology, which is not safe and effective without a computed path. The purpose of route guidance is to gather information from outside sources (e.g. other vehicles, fleet signals) and stored data to create the most efficient route. However, this technology is hindered by the limited amount of information that can be stored within the vehicle due to static map conditions.⁵ Static conditions are defined as the basic components of individual roadways, such as the length of the road, speed limit, and pre-existing intersection signals. Using static systems can result in unreliable and slower routes due to an inability to account for dynamic road situations; for example, these static routes can be highly ineffective once an accident occurs on the roadways.

Generating accurate routes while on the road is another computationally challenging problem for autonomous technology.⁵ Due to the mobile condition of autonomous vehicles, current onboard computational power cannot compute and translate both long algorithms and dynamic conditions at the same time. Researchers attempting to create an algorithm must balance quick execution and efficient route creation with low computational power.

An additional problem arises from dynamic roadways. Dynamic roads are defined as streets that are always changing due to traffic jams, accidents, and construction.⁵ In his article on route guidance, Yanyan Chen stated that a good route is one that, although possibly not the fastest, is both reliable and acceptable to the driver’s needs. As a solution, Chen and his team created the Risk-Averse A' Algorithm (Figure 1). This algorithm suggests a risk-averse strategy that pre-computes factors that affect traffic (such as weather and time of day), accounts for dynamic traffic flow and accidents, and computes a low-risk and reliable route. The Risk-Averse A' Algorithm is widely accepted in the field of autonomous research as the most efficient form of computing reliable and adaptive directions. In fact, Stanley used this algorithm in the DARPA Challenge.⁴

Navigating Intersections Proves Difficult

The task of navigating an autonomous car through an intersection is not simple. The vehicles must be able to use algorithms to derive not only the distance from the car to the intersection but also its current inertia. Simultaneously, this information must be constantly compared with that of other vehicles. The two main challenges in crossing an intersection are establishing reliable communication with other vehicles as well as the dynamic, convoluted environment of intersections. For autonomous navigation to be possible, vehicles must communicate with each other to determine which car has right of way. When approaching an intersection, each car should propagate signals to the other vehicles, a failsafe in case oncoming cars are not detected by the visual and laser system (Figure 2). In theory, autonomous vehicles will discharge signals containing position and velocity information. At an intersection, approaching cars can detect and process this information to determine the appropriate mechanical move.

The dynamic environment of an intersection creates a whole new series of problems with the introduction of unknown variables. An autonomous system must be able to adapt, sense, and make decisions in short periods of time. The proposed ideas on how to navigate intersections use a decentralized navigation function, a method that has no need for long-range communication between vehicles. It enables cars to navigate independently while maintaining network connectivity and an overall goal. This function allows the car to account for dynamic traffic and improves the use of algorithms.²

Robotic Communication

The problem of real-time coordination between vehicles is a major obstacle that must be overcome for this technology to function safely on city streets and highways. Without reliable and fast communication, autonomous vehicles cannot navigate intersections, conserve energy, drive in safe formations, or create efficient routes. However, communication through wireless networks is not always reliable. Dr. Mélanie Bouroche from Trinity College, Dublin, stated that a “vehicle intending to cross an un-signaled junction needs to communicate in an area wide-enough to ensure that other vehicles … will receive its messages.”³ Figure 3 illustrates how the cars should disperse signals to communicate with other vehicles.

In the article “Real-Time Coordination of Autonomous Vehicles,” Bouroche, Hughes, and Cahill found a solution to this communication issue by creating a space-elastic communication model. A coordination model for autonomous cars allowed autonomous vehicles to adapt their behavior depending on the state of communication, ensuring safety constraints were never violated.³

Conclusion

Autonomous technology should improve daily travel by decreasing fuel consumption, traffic congestion, and accidents. The construction of new highways and streets to accommodate this technology would modernize and improve the efficiency of cities. Daily life could be enhanced, as driving time could be spent more productively. Autonomous technology can greatly improve everyday vehicular travel—but only if it is correctly implemented into society. Many problems still remain in the realization of autonomous vehicles: detection systems must be improved to effectively identify and avoid obstacles, algorithms need to be refined to quickly compute dynamic routes, and communication between vehicles needs to be drastically improved in order to avoid accidents. The legal and societal issues must also be addressed: will all vehicular travel be converted to automated travel? If so, will all citizens be forced to use technology that controls their movement? If not, will separate highways and roads be built? Who will fund this new creation of streets and roads? Who will ultimately control and maintain such a system? Autonomous technology has the potential to vastly improve travel, but it can introduce system vulnerabilities and malfunction. Self-directed vehicles must be thoroughly researched and tested before the technology can be implemented on city, state, and national streets.  

References

1. Hayes, B. Am. Sci. 2011, 99, 362-366.
2. Fankhauser, B. et al. CIS 2011: IEEE 5th International Conference, Qingdao, China, Sept 17-19, 2011; pp.392-397.
3. Bouroche, M. et al. IEEE Conference on Intelligent Transportation Systems, 2006, 1232-1239.
4. Thrun, S. et al. J. Field Robot. 2006, 23, 661-692.
5. Chen, Y. et al. J. Intell. Transport. S. 2010, 14, 188–196.
6. Bergenhem, C. et al. Sartre, 2008, 1-12.
7. Dahlkamp, H. et al. In Proceedings of the Robotics Science and Systems Conference, 2006, 1-7.
8. Elliott, C. et al. In The Royal Academy of Engineering, 2009, 1-19.
9. Douglas, G. W. Unmanned Systems, 1995, 13, 3.
10. Laugier, C. et al. In Proceedings of the IFAC Symposium on Intelligent Autonomous Vehicles. 2001, 10-18.
11. Wright, A. Comm. ACM 2011, 54, 16-18.