Chicago’s O’Hare Airport. An ANA 777 waits at Gate 16A for departure for its long haul to Tokyo’s Narita airport.

The beauty of aviation never ceases to amaze.

Flight navigation has a rich history. It evolved from basic celestial navigation to advanced GPS systems. Some key milestones include:

1. Celestial Navigation: Early aviators relied on stars and landmarks for orientation.
2. Radio Navigation: Radio beacons and VOR (VHF Omnidirectional Range) stations improved accuracy.
3. Inertial Navigation: Developed during WWII, inertial systems used gyroscopes and accelerometers.
4. GPS: The Global Positioning System, developed by the U.S. military, became the foundation of modern aviation navigation.
5. Flight Management Systems: Advanced computers integrated navigation, flight planning, and automation.
6. NextGen: Ongoing modernization efforts aim to improve efficiency and safety using advanced technology.

These advancements have made air travel safer and more efficient over the years.

Flight Management Computers (FMCs) have played a crucial role in revolutionizing aviation by automating navigation, reducing pilot workload, and enhancing flight efficiency. The evolution of FMCs is a story of innovation, integration, and the seamless fusion of computer technology with aviation.

The concept of automated flight management dates back to the 1950s, when the airline industry recognized the need for improved navigation and flight planning systems. Early systems were rudimentary, relying on analog computers and basic navigation aids.

In the 1970s, the advent of digital technology paved the way for more advanced FMCs. These systems began to appear in larger commercial aircraft, offering functionalities such as route optimization, altitude and speed control, and fuel management. The Boeing 767, introduced in 1982, was one of the first aircraft to incorporate a fully integrated FMC.

By the 1980s and 1990s, FMCs had become standard in many modern aircraft, providing pilots with the ability to program routes, calculate fuel requirements, manage the autopilot, and handle various flight phases. These computers relied on databases of waypoints, airways, and airports, enabling precise navigation even in complex airspaces.

The turn of the century brought about even more sophisticated FMCs. Integrated with advanced satellite-based navigation systems like GPS, FMCs could accurately determine an aircraft’s position in real-time, allowing for precise navigation along curved paths and optimized routes. This marked a significant leap in efficiency and safety.

In recent years, FMCs have evolved to address modern challenges such as fuel efficiency and environmental impact. Airlines and aircraft manufacturers are increasingly focusing on developing FMC software that considers factors like wind patterns, engine performance, and cost-effective routing to minimize fuel consumption and emissions.

Looking ahead, the integration of artificial intelligence, machine learning, and advanced data analytics is likely to shape the next phase of FMC evolution. These technologies could enable FMCs to predict and adapt to weather conditions, optimize routes dynamically, and further reduce human intervention while ensuring safety remains paramount.

Moreover, the rise of digital connectivity has enabled the transfer of real-time data between the aircraft and ground systems, facilitating better decision-making and operational efficiency. Today’s FMCs are more intuitive, user-friendly, and capable of adapting to changing conditions, enhancing the pilot’s ability to manage the flight effectively.

In summary, the history of Flight Management Computers is a testament to the ongoing synergy between aviation and technology. From humble beginnings to cutting-edge automation, FMCs have transformed the way aircraft navigate the skies, making air travel safer, more efficient, and more environmentally conscious.

CP JOIS

Tiger teams have been long used for problem-solving and or responding to opportunities (Laakso et al., 1999). Tiger teams are not just another form of a team with a set of resources. They are formed differently, used for episodic actions, and then released when the task is complete. In most cases, tiger teams are used for solving difficult problems in a timely way. Given this reason. these teams are formed with a purpose and bring specific skills (Pavlak, 2004). They are also created to self-sufficient and complete in terms of skills and capacity.

As a manager researching flight management system (FMS) issues, I would bring a group of resources with the required skill sets and more importantly experience. The skills needed would include, hardware, software programming, testing, and automation. I would also include subject matter experts from flight control, navigation, avionics, navigation data, and computational expertise. The team will also comprise resources from external partners for hardware, the operating system, and any other sub-components that were externally sourced. I would also ensure that escalation paths are clearly identified within each of the external organizations in case, such escalations were required to ensure priority and timeliness in those organizations.

Once the team has been formed, interaction cadence is important. When and where will they meet, how often and who leads the team – becomes important. Establishing a team leader helps with this process. Setting up a ‘war room’ will be essential to collate all the necessary design artifacts and incident reports. that will help troubleshoot the issue. A flight management system simulator will be required and set up. The simulator will help in reproducing the navigation guidance issue(s).

Robust system design “ensures that future systems continue to meet user expectations despite rising levels of underlying disturbances” (Mitra, 2010). The intent of the robust design is to allow a system to function reliably, perhaps with reduced capability, despite errors in input or computation. Systems are designed to accommodate a vast range of operating conditions and inputs. Despite that, every system has its limits where outside of that operating envelope, the system does not have the intelligence to handle the situation (Atkins et al., 2006). In the given FMS situation, without more detail, it’s hard to conclude that the system is either robust or not. Regardless of how holistic and intelligent a system is, once outside of its programmed boundary, the system would not know how to handle a specific problem. In 2008, Qantas flight 72 suffered an uncommanded loss in altitude now associated with a bit being flipped in the flight management system caused by ionization radiation (See Baraniuk, 2022 for more information on computer bit flips due to solar radiation associated solar flares). This is an example of ‘yet to be known’ factors that could impact the robustness of a system.

Typically redundancy is used as a mitigation for safety-critical systems. Having backup systems is an effective strategy for dealing with insufficient robustness (Mitra, 2010). Multiple of input sensors allow for differentiation models to detect differences and when supplemented with a tertiary sensor, allow for triangulation and therefore detection of an impending problem (Bijjahalli et al., 2020). In the case of software systems, where complex logic can be the cause for errors, exhaustive testing of all code branches and automated testing of multiple-programmatic paths is typically used to prevent an isolated line of code to cause an error (Huhns, & Holderfield, 2002). Data is another cause for the lack of robust behavior because systems are as precise as the data provided. Data verification and validation are the mitigation for this cause.

Ideally, it is best to build systems such that if it’s unable to compute an answer within its defined operating envelope, it must signal such failure to the crew and allow them to resolve the situation. That said, differential input computations led to the autopilot on Air France 447 disconnecting at 35000 feet over the Atlantic leaving the plane in the crew’s hands (Admiral Cloudberg, 2021). In the final analysis, the crew stalled the airplane and all data indicates that the controls were held in high pitch position all the way to its final impact. This indicates that using reverting to manual control as a means of robust design may also not be the best answer for all situations.

Human input is a common problem and protecting a system from erroneous input is perhaps the most significant challenge for designers (Atkins et al., 2006). To anticipate the various inputs that numerous users of a system could potentially input into a system is an extraordinary challenge for any designer. American Airlines flight 965 to Cali, Colombia impacted terrain from an erroneous input into the FMS (Ladkin, 1996). Rushing through an approach at an airport without operational radar, accepting a different approach than earlier planned and programmed, clearing the programming from the FMS to execute a visual approach, and rushing through FMS waypoint entry without verification with associated charts are reported to be the most probably causes (Pérez-Chávez & Psenka, 2001). There are more causes that came together as explained in Reason’s Swiss cheese model that contributed to the crash (Reason et al., 2006). Other factors included a single letter identification for a navaid 150 miles away which with some diligence and attention, could have been easily detected if the crew was not rushing. A single letter – R – indicating two different navaids ROMEO and ROZO – caused the airplane to make a sharp left turn and head straight into the terrain. Preventing error input is typically used to maintain the operational boundaries of a system. However, this could prove limiting in itself.

The first task for the team will be to reduce the errors as much as possible. This allows for a close study of the problem. Documenting the causes in fishbone diagrams allows for listing all causes that could have led to the issues. Taking each case individually to further resolve them would lead to resolution of the issue. The benefit of using a tiger team for this purpose is to have undistracted bandwidth to focus on the issues on hand.
There is no perfect answer to designing robust behavior. It is as much art as it is a science to build a complex, comprehensive design. Achieving perfect design is an ongoing challenge and it is worthy of mention that automation and human factors issues remain a serious concern for the aviation industry even today.

References:
Admiral Cloudberg. (2021, October 9). The Long Way Down: The crash of Air France flight 447. Medium; Medium. https://admiralcloudberg.medium.com/the-long-way-down-the-crash-of-air-france-flight-447-8a7678c37982Links to an external site.
Atkins, E. M., Portillo, I. A., & Strube, M. J. (2006). Emergency Flight Planning Applied to Total Loss of Thrust. Journal of Aircraft, 43(4), 1205–1216. https://doi.org/10.2514/1.18816Links to an external site.
Baraniuk, C. (2022, October 12). The computer errors from outer space. Bbc.comLinks to an external site.; BBC. https://www.bbc.com/future/article/20221011-how-space-weather-causes-computer-errorsLinks to an external site.
Bijjahalli, S., Sabatini, R., & Gardi, A. (2020). Advances in intelligent and autonomous navigation systems for small UAS. Progress in Aerospace Sciences, 115, 100617.
Huhns, M. N., & Holderfield, V. T. (2002). Robust software. IEEE Internet Computing, 6(2), 80-82.
Ladkin, P. (1996). AA965 Cali accident report. University of Bielefeld.
Laakso, M., Takanen, A., & Röning, J. (1999, June). The Vulnerability Process: a tiger team approach to resolving vulnerability cases. In Proc. 11th FIRST Conf. Computer Security Incident Handling and Response.
Mitra, S. (2010). Robust System Design. 2010 23rd International Conference on VLSI Design, 434–439. IEEE. https://doi.org/10.1109/VLSI.Design.2010.77Links to an external site.
Pavlak, A. (2004). MODERN TIGER TEAMS.
Pérez-Chávez, A., & Psenka, C. (2001). Systems accidents and epistemological limitations: The case of American airlines’ flight 965 in Cali, Colombia. Practicing anthropology, 23(4), 33-38.
Reason, J., Hollnagel, E., & Paries, J. (2006). Revisiting the Swiss cheese model of accidents. Journal of Clinical Engineering, 27(4), 110-115.
Pavlak, A. (2004). Modern TIger Teams.

What comes to mind when you think of ‘good instruction?’
Instruction basically means to direct or help acquire a skill or help ‘do’ something.

Good instruction is about providing steps on ‘how to do’ something in the simplest but most effective manner. Ultimately the test of good instruction is how quickly a learner can acquire the skill being taught and how effectively that Individual can demonstrate gained proficiency.

What were your most profound (positive or negative) learning experiences?
The one experience that comes to my mind is my flight training experience.

My instructor was an individual who learned to fly in Hawaii, in times when flying was not as regulated or complicated. Making learning a fun experience was his primary goal. He deeply believed that when you enjoy something, you learn faster. He also grew up in aircraft that were basic. Hence his stick and rudder skills were so much more effective. He was a natural at flying. More than all the theory he provided me (which I got from my textbooks also), his attitude towards flying and instructing struck me as most powerful. I went from zero to solo in 20 days. He would wake up early so that I could fly early mornings before I got to work. We flew every morning at 5:30a or 6a. He would demonstrate how every runway, however short, was long enough if the proper technique was applied.

I got my PPL. However, I took away more about attitude and instruction from him, than simply flying skills.

How does it feel to teach someone how to do something?
I have always had a passion to explain concepts, events, machines, and weather phenomena to those interested in it. I have a natural ‘coaching’ style when it comes to building teams at work. I have had the privilege of running 300-400 person teams and my instinctive style is to coach and allow individuals the latitude to express themselves. I run a flight simulation venture that I started out of a deep passion for simulator technology combined with a passion for teaching/instructing. I began teaching at a university in Chicago over a decade ago only because I wanted to ‘pay it forward’. Shaping minds, and creating the next generation of professionals give me immense pleasure. One-half of my 30-year career has been in building and operating technology platforms for Higher Education. In that context, I was privileged to develop algorithms for adaptive learning and competency-based learning models. I used it as an opportunity to deploy my learning as a student and an instructor into new models for learning and instructional design. To me, teaching and/or instructing a learner is a profoundly rewarding experience. This is also the reason why I took an adjunct role at ERAU.

When teaching someone how to do something, what strategies do you use?
Modeling elements from the ‘real environment’ is essential. Creating an environment that mirrors the environment in which the knowledge or skill will be applied is essential for the effective transmittal of knowledge or skill. Hence I try to recreate elements from the ‘real environment’ in the teaching process.

I also enable a student as many learning aids as possible.

Different learners learn differently. Hence teaching style has to adapt and I adapt as needed. For some, visual aids are effective, others learn better by listening and some do well by ‘doing’. I use any or all of these channels.

Explaining underlying theory to the extent needed substantiates learning. Mixing theory with practice is another strategy for the effective transmission of knowledge. The learner must feel the joy of learning something. Being able ‘to do’ something, and being able to apply the knowledge or skill is very effective in reinforcing learning. There are times when I create a phenomenon and I ask the student to explain why it’s occurring.

Chunking learning into smaller segments is another strategy. Especially when learning is ‘chunked’ into segments that collectively and cohesively aggregate to a larger whole, knowledge or skill is transmitted effectively.

Current-day technology allows for several techniques to ‘gamify’ learning and bring a sense of challenge into the learning process. The human psyche likes a challenge – however, care must be exercised to ensure that it is not perpetually overwhelming where it can introduce a sense of “I can never win this’. Hence adaptive learning is powerful. Using Machine Learning techniques, the system can be engineered to adapt to skills/success levels and introduce the challenge in a controlled manner where the learner is challenged, but a little bit at a time, and knowledge or skill is built over time.

How do you know if a learning experience was successful?
Measured assimilation is the true test of success in learning. Can the learner explain a concept effectively and have an audience understand it? Can a student now fly effectively and within standards? These are examples of success in learning experiences.