Category: simulators

Simulator Benefits

~ cjois

There is little doubt that simulators have redefined the realm of initial and recurrent training in both Military and Commercial aviation. Cost benefits have been a primary consideration. Lowering the risk of training has been the other major benefit. Achieving balance between simulator and real-aircraft training time has been a subject of much debate and research. Leaning too much to either format has impact. On one side, cost impacts could be significant. On the other, the trainee has little feel for what it is like to be performing this tasks in a real aircraft.

There is also truth to the fact that some areas of training are better handled in a sim while others absolutely need an aircraft. In my opinion, simulators have evolved to a point where they are close to ‘as real as it gets’. Transfer of training has proven to be effective. Aircrafts have become more technically advanced and a lot of training is focused on procedure and automation – an area where sims lend themselves to really well.

Replication of real-world weather, comms, terrain, flight dynamics have become possible. There isn’t a lot of loss in ambient factors in a simulator today.

In fact the term ‘supplement’ almost implies that sims are secondary. That has changed with time. In many areas, simulators end up being primary channels for training while aircraft-based training come in at an equal percentage or less.

Again, the one major risk of doing too much time in a sim is that it may lead to a situation where the trainee has little or no feel for what the real world circumstances will be like. This too, then comes down to how well real world factors are modeled into a simulation ecosystem – aka fidelity.

Cognitive Psychology in Aviation

~ cjois

Failures in prospective memory (PM) are the reason why we fail to perform intended or required actions. There is increasing interest in the topic of prospective memory and the reasons for failures of such memory. While this subject is still under intense debate, according to one school of thought, prospective memory recall is driven by the process of monitoring. Another view is that it occurs as part of spontaneous retrieval.

In either case, the intention for the planned task is retrieved which then allows for action. Distractions are one source of why action is forgotten. Interruptions of any kind can be a cause (Shorrock, 2005; Sternberg & Sternberg, 2016). A telephone call or request for information can be sufficient cause to not return back to the ongoing task. The variety of peripheral tasks that controllers need to perform often conflict with the primary task of maintaining separation. Such tasks could include scanning displays, accepting aircraft, gathering and relaying weather advisories and responding to pilot requests.

Prospective memory recall is predicated on cues. A cue or trigger is necessary for prospective memory to work. As described earlier, to recall the intent, the human mind constantly polls for such items. When polling is not invested in, such as when we are preoccupied with other task(s), then the intent is not recalled and action is termed as ‘forgotten’. Under another school of thought, spontaneous retrieval occurs on account of a system within our brain that causes automatic retrieval of items at the appropriate times. Once again, when tasks preoccupy, spontaneity drops and we tend to forget the intent. Proximity, recency and task regularity could all affect prospective memory (Vortac, Edwards & Manning, 1995).In the context of ATC, prospective memory failures can prove to be catastrophic.

The incident at San Francisco of a controller positioning an aircraft on the runway for takeoff, forgetting about it, and further clearing an aircraft to land on the same runway is a case in point (Loft, 2014). They can affect controller actions such as separation, scope monitoring or performing other tasks such as flight strip updates, aircraft transfer, peer collaboration and shift transitions. Inaccurate recall of information on a strip, failing to observe conflicts and failure to annotate strips correctly are all examples of PM failures. Controllers may intend correctly but then fail to follow through on that thinking because they simply “forgot to do so”. In the realm of ATC, cues are either based on time or based on events (Loft, 2014; McDaniel & Einstein, 2007). However, monitoring takes a cost in the form of “brain cycles” and therefore impacts performance. Such impacts could come in the form of slowing down a certain action in order to devote time to monitoring.External cues are an effective way to mitigate the risks of prospective memory failure (Vortac & Edwards, 1995).

Memory aids are useful and can be any tool, prop or other aid that could serve as a reminder (FAA Video, 2015). They need to be incorporated into the routine though and not be ad-hoc. Mnemonics and placards are one way to avoid prospective memory errors (Loft, 2014; Stein, 1991). Using free text to jot down notes is another option. Memory aids must be effective. A good example from the video is that of holding a strip in hand as a reminder when there is a vehicle inspecting the runway.

There is a growing interest in having the system alert and warn if an action is overdue. The sophistication available today makes it possible to code rules into the system and have it warn the controller. However, this may lead to the same type of over dependence on automation and sense of complacency that we find occur in pilots. 

References

Federal Aviation Administration. (2015, September 02). Retrieved April 25, 2017, from https://www.faa.gov/tv/?mediaId=1151

Federal Aviation Administration. (2015, September 02). Retrieved April 25, 2017, from https://www.faa.gov/tv/?mediaId=1152

Loft, S. (2014). Applying psychological science to examine prospective memory in simulated air traffic control. Current Directions in Psychological Science, 23(5), 326-331.

McDaniel, M. A.. & Einstein G. (2007). Prospective Memory. Thousand Oaks: SAGE Publications. Retrieved from https://ebookcentral.proquest.com/lib/erau/detail.action?docID=996509

Shorrock, S. T. (2005). Errors of memory in air traffic control. Safety science, 43(8), 571-588.

Stein, E. S., & Federal Aviation Administration Technical Center (U.S.). (1991).

Air traffic controller memory: A field survey. (). Springfield, Va;Atlantic City International Airport, N.J;: Federal Aviation Administration Technical Center.

Sternberg, R. J., & Sternberg, K. (2016). Cognitive psychology. Nelson Education.

Vortac, O. U., Edwards, M. B., & Manning, C. A. (1995). Functions of external cues in prospective memory. Memory, 3(2), 201-219.

Fatigue on the FlightDeck

~ cjois

Generally speaking, ‘Fatigue’ is predominantly influenced by sleep loss and circadian rhythm disruptions (Salas & Maurino, 2007). Fatigue is not a problem that is specific to one area of Aviation. All forms of aviation are at risk. While much of the research focuses on long-haul aviation, a lone GA pilot battling cognitive overload can quickly turn into a fatigue-crisis (Guastello et al., 2012). In addition, few General Aviation pilots have adequate training or resources to detect onset, and/or remedy, Fatigue (Harris et al., 1995) 

GA would benefit from a simple model that can consume simple parameters such as flying conditions, route of travel,  pilot health, sleep history, pilot flying history etc. and provide a risk score to a pilot based on which a decision to fly can be made.

 I believe that even knowing that there is a level of risk given all the parameters that exist is a great thing to have. The IMSAFE checklist is good, however, when one goes through the checklist  it is indeed hard to have a true assessment. I have seen many times that GA pilots run through teh checklist quickly and decide to fly. However, I have often thought whether a GA Pilot would reject a decision to fly based on knowing that the pilot has had a growing sleep deficit over the past week or month; or whether a forecast indicated sharp temperature drop between altitudes (indicating turbulent air in that region) combined with a sleep deficit should deter a pilot from flying that day. 

Fatigue can occur pretty rapidly even in a fully fit individual in a GA cockpit (with little automation). When combined with other factors, the situation can unravel very quickly (Salas & Maurino, 2010). I know from experience that there have been days when I have gone out for a recreation flight in the local area and after battling turbulent air in single piston aircraft for 90 minutes, I have landed and felt really worn out from the experience – add a situation of 4-5 hours of sleep the prior night and this fatigue multiplies multi-fold.

They highlight the mission-critical dependence on human performance in some industries or professions. I don’t believe that this dependence, or impact,  is even comprehended by most outside these professions. I have felt that even working for an airline experiencing the pressures involved in keeping a real-time operation running optimally does not fully clarify the complexity. The body of literature on this topic is immense and just reading a few of the papers (infinitesimal, compared to the literature available) on the subject of shift scheduling in some industries has evolved my thinking on the topic. The references below indicate some of the papers that I found very helpful in getting to understand some basic facets of this subject. The integration of fatigue models into scheduling algorithms was a very interesting topic (Ta-Chung & Cheng-Che, 2014). One conclusion I draw… scheduling in some industries is not merely about managing time and people. It is multi-dimensional and mission-critical. 

References

Barton, J., & Folkard, S. (1993). Advancing versus delaying shift systems. Ergonomics, 36(1-3), 59-64. doi:10.1080/00140139308967855

Caldwell, J. A., Mallis, M. M., Caldwell, J. L., Paul, M. A., Miller, J. C., Neri, D. F., & Aerospace Medical Association Fatigue Countermeasures Subcommittee of the Aerospace Human Factors Committee. (2009). Fatigue countermeasures in aviation. Aviation, Space, and Environmental Medicine, 80(1), 29-59. doi:10.3357/ASEM.2435.2009

Guastello, S., Boeh, H., Schimmels, M., & Shumaker, C. (2012;2011;). Catastrophe models for cognitive workload and fatigue. Theoretical Issues in Ergonomics Science, 13(5), 586-17. doi:10.1080/1463922X.2011.552131

Harris, W. C., Hancock, P. A., Arthur, E. J., & Caird, J. K. (1995). Performance, workload, and fatigue changes associated with automation. The International Journal of Aviation Psychology, 5(2), 169-185. doi:10.1207/s15327108ijap0502_3

Knauth, P. (1996). Designing better shift systems. Applied Ergonomics, 27(1), 39-44. doi:10.1016/0003-6870(95)00044-5

Salas, E., & Maurino, D. E. (2010). Human factors in aviation (2nd ed.). Boston, Mass;Amsterdam;: Academic Press/Elsevier.

Smith, L., Hammond, T., Macdonald, I., & Folkard, S. (1998). 12-h shifts are popular but are they a solution?International Journal of Industrial Ergonomics, 21(3), 323-331. doi:10.1016/S0169-8141(97)00046-2

Ta-Chung, W., & Cheng-Che, L. (2014). Optimal work shift scheduling with fatigue minimization and day off.Mathematical Problems in Engineering, doi:http://dx.doi.org/10.1155/2014/75156

Building a Motion Platform – the basics

~ cjois

For many years now, building a motion platform for a recreational flight simulator has been on my mind. Extending a home-built recreational simulator with a motion seat or motion base is nice science project but is also a meaningful extension to the study of the simulators. Three years ago I had built a small servo-based model of a motion platform with 2 degrees of freedom (2DOF).

https://youtu.be/PFR2ZPfaMWM

Three weekends ago, I started down the path of figuring out a design to make a scale version of a motion platform.

There are several parts to the development of a motion platform that can connect to a PC-based simulation engine. I have had very little experience with any of these steps and so it had to be learning by doing.

The overall set of steps are as follows –

  • Determining how to communicate with a real servo or motor or actuator 
  • Determining whether to use motors, servos or actuators
  • Validating the hardware cards that could interface between a PC and those motors or actuators
  • Writing code to drive those cards that drive those actuators or motors.
  • Powering actuators or motors or servos
  • identifying the right actuators or motors or servos that will serve the platform build
  • Connecting the actuators to the interface cards and then to the PC
  • Writing test code that tests the actuators
  • Connecting the game to simulation platform engine
  • Connecting the platform engine to hardware driver engine
  • Acquiring and transmitting telemetry to the chain above so as to be able to get the game’s motion to reflect realistically on the actuators.

Note that none of the above yet even discusses the build of a platform base or seat. This is just the work that is needed to get the concept validated.

For the simple prototype I chose to go with Progressive Automation for actuators. I also chose to go with MultiMoto Motor/Actuator driver. This card would seamlessly integrate with an Arduino chip. I picked up the LA-14P actuator from Progressive because it had built-in feedback. Needed a power supply and used my 10 Amp, 12V power supply that I use to charge my RC airplane batteries.

Until next time…

CJ

Designing the machines that build the machine

~ cjois

Innovating new concepts and creating new products has been a common and consistent theme in the industry. It is interesting to note that when such innovation occurs in an new industry, many of the corresponding methods, mechanisms, equipment do not exist. For example, when Boeing struck an agreement with the chief of PanAm back in the 60s to build a bigger jet than was available at the time, apart from the design of a new aircraft, they had to evolve, build and validate all other components that led to the delivery of the 747. They pretty much put the company on the line in doing so bringing them close to bankruptcy at one point.

There are several such examples in the history of aviation. Indeed, such innovation has been cyclical and the industry has gone through many such cycles of peaks of intense innovation and then periods when they have basically struggled to stay afloat. This discussion is important because the evolution of the simulator is one such innovation. The simulator was an outcome of need – the need to train people on what was built. With time, it turned into a tool – a tool to help address the need to test what was built. In both cases, minimize risk, then minimize cost and then provide a platform to scale operations.

Among the various examples we have seen/read about, I find FAA’s NextGen use of simulators to be a comprehensive example. I find it comprehensive because of various, multi-faceted elements that NextGen reaches into. there are changes to aircraft, airports, traffic control, navigation, communications, crew roles, training processes and a whole lot more. There have been many who have questioned if such a wide impact program is even safe to implement as one program. FAA’s thinking has been that there comes an inflection point when multi-path changes are required to be performed in tandem rather than piecemeal.

Come to think of it, simulators have changed character over the past century. They have gone from helping test/train the machine they model TO helping with modeling (designing) the machine itself.
In the case of NextGen, the future machine is a redesigned USNAS.

Designing simulators that help design the future airspace system is a complex endeavor – fraught with risk. Often, its harder to design the simulator than it is to implement the model in the real world. More importantly, validating such simulators to ensure that they are accurate enough to model the real thing is a complicated exercise. Simulator-related research over the past 5 decades is a mix of successes on one side; and criticisms and warnings on the other side. There are many studies providing us data that simulator design is an evolving science – and that an over-reliance on simulators can lead to problems. In the light of persisting concerns, the use of simulators to design an overhaul of the USNAS can actually be questioned.

Are these simulators able to adequately model and predict behavior in the real world. Are we leaving something out of the model that is in fact a part of the real world environment? Is the simulator violating one of the core principle of learning design, i.e. modeling of identical elements?…
While being a passionate advocate of simulators, I find some of these persisting concerns problematic and in need of expeditious study.
CP

Supplementing flight time with simulation time

~ cjois

There is little doubt that simulators have redefined the realm of initial and recurrent training in both Military and Commercial aviation. Cost benefits have been a primary consideration. Lowering the risk of training has been the other major benefit. Achieving balance between simulator and real-aircraft training time has been a subject of much debate and research. Leaning too much to either format has impact. On one side, cost impacts could be significant. On the other, the trainee has little feel for what it is like to be performing this tasks in a real aircraft.
There is also truth to the fact that some areas of training are better handled in a sim while others absolutely need an aircraft.
In my opinion, simulators have evolved to a point where they are close to ‘as real as it gets’. Transfer of training has proven to be effective. Aircrafts have become more technically advanced and a lot of training is focused on procedure and automation – an area where sims lend themselves to really well.
Replication of real-world weather, comms, terrain, flight dynamics have become possible. There isn’t a lot of loss in ambient factors in a simulator today.
In fact the term ‘supplement’ almost implies that sims are secondary. That has changed with time. In many areas, simulators end up being primary channels for training while aircraft-based training come in at an equal percentage or less.
Again, the one major risk of doing too much time in a sim is that it may lead to a situation where the trainee has little or no feel for what the real world circumstances will be like. This too, then comes down to how well real world factors are modeled into a simulation ecosystem – aka fidelity.

Role of Simulators in FAA’s NextGen program

~ cjois

Simulation ecosystems are used in a variety of applications beyond their use in training of pilots. While simulators were initially used to help train pilots, they rapidly evolved into playing important roles in advancing aviation overall. Human factors assessments, aircraft and airport design, flightdeck instrumentation design, operating procedure development, air traffic control training, air traffic flow management evaluations are some examples of where simulators are used outside of the realm of direct pilot training (Lee, 2005).

A specific current day example of the use of simulators outside of pilot training is in FAA’s NextGen Program. Air traffic is expected to increase over the next 15 to 20 years and the “NextGen” Program is a comprehensive overhaul of the US National Airspace System to respond to the upcoming demands. NextGen introduces revolutionary new approaches to capacity problems. It will use newer technologies and automation to shift the way air traffic is managed. NextGen is not one idea, but a series of initiatives aimed at transforming different aspects of the aviation ecosystem (Federal Aviation Administration, 2014). The program has been structured into a set of program areas, typically focused on laying out infrastructure. These areas include Automatic Dependent Surveillance Broadcast (ADS-B), En Route Automation Modernization (ERAM), Data Communications (DataComm), National Airspace System Voice System (NASVS), Terminal Automation Modernization and Replacement (TAMR), and System Wide Information Management (SWIM). NextGen also has a set of portfolios that deliver new capabilities. The portfolios are Time-based Flow Management, Collaborative Air Traffic Management, Improved Approaches and Low-Visibility Operations, Improved Surface Operations, On-Demand NAS Information, Performance-based Navigation, Improved Multiple Runway Operations, Separation Management, and Environment & Energy (Federal Aviation Administration, 2014).

Clearly, the NextGen program will advance commercial aviation in the US and serve as a role model for other such implementations. It will also require changes that could impact the design of future aircraft, air traffic control processes and devices, airport layouts and maintenance facilities, training content, training processes, job aids and performance support systems. The NextGen program will rely heavily on the use of simulation environments to design and test the necessary changes (Callantine, 2008; Crutchfield, 2011; Doucet, 2013; Hunter, 2009). Many of the proposed changes need to be tested before implementation begins, but it is difficult to conduct human factors tests on an environment that does not yet exist. The use of synthetic environments in these situations bring benefits in terms of cost and risk. There is significant benefit to being able to simulate scenarios and test out human interaction with machines before their use in real-world environments.

One very specific example is the use of NextSim. NextSim is an ATC research simulator that collects performance, workload, and situation awareness data to address human factors/ ergonomics issues that might arise in NextGen (Durso, Stearman, & Robertson, 2015). Another example is where, according to a Rockwell Collins’ release, a Boeing 737 flight simulator in the FAA’s Flight Operations Simulation Laboratory (FOSL) in Oklahoma City, will be used to study the viability for NextGen to safely achieve benefits such as lower landing minima by using Rockwell Collins head-up displays with synthetic and enhanced vision during different phases of flight in low visibility conditions (“FAA chooses Rockwell Collins’ guidance systems”, 2012). At Oshkosh AirVenture 2010, the FAA NextGen Data Communications (DataComm) program demonstrated by using simulators that new Data Comm technology will deliver major savings in time, money, fuel, as well as, environmental effects. The technologies introduced by DataComm included its new air traffic control (ATC) and Boeing 737 cockpit simulators (Gonda & Zillinger, 2010).

Callantine (2008) describes the use of simulation to analyze human-in-the-loop route structure simulation data. Hunter (2009) describes the design and test of the simulators for use in NextGen, and further proposes test protocols for NextGen simulators. Doucett (2013) details out a cross-organization effort to setup a distributed environment comprised of aircraft and ATC simulator that can serve as a collaboration tool for NextGen design and test. Prevot, Homola, and Mercer (2008) study the trajectory based operations, a NextGen component using simulators.

Based on the discussion above, there is little doubt that simulators and synthetic environments have, and continue to play, a critical role in aviation, over and beyond their use for direct pilot training.

References

Callantine, T. (2008). An integrated tool for NextGen concept design, fast-time simulation, and analysis. In Proceedings of the AIAA Modeling and Simulation Technologies (MST) Conference, Honolulu, HI.

Crutchfield, J. M. (2011). NextGen update. Aviation, Space, and Environmental Medicine, 82(9), 925-925. doi:10.3357/ASEM.3117.2011.

Doucett, S. (2013). Distributed environment experiment for NextGen. doi:10.2514/6.2013-4277.

Durso, F. T., Stearman, E. J., & Robertson, S. (2015). NextSIM: A platform-independent simulator for NextGen HF/E research. Ergonomics in Design, 23(4), 23-27. doi:10.1177/1064804615572624.

FAA chooses Rockwell Collins’ guidance systems with synthetic and enhanced vision to support NextGen efforts. (2012). Entertainment Close-Up.

Federal Aviation Administration. (2014). NextGen Implementation Plan 2014. Retrieved from https://www.faa.gov/nextgen/library/media/NextGen_Implementation_Plan_2014.pdf

Gonda, J., & Zillinger, E. (2010). Digital avionics. Aerospace America, 48(11), 44.

Hunter, G. (2009) Testing and validation of NextGen simulators. doi:10.2514/6.2009-6124.

Lee, A. T. (2005). Flight simulation: Virtual environments in aviation. Burlington, VT;Aldershot, England;: Ashgate.

Prevot, T., Homola, J., & Mercer, J. (2008). Initial study of Controller/Automation integration for NextGen separation assurance. () doi:10.2514/6.2008-6330.

 

The networked simulator

~ cjois

Over the past 6 months i have done so much work on my simulator that it made me think about writing this post on the compelling possibilities that arise from a networked simulator and a network of simulators.

Just over the past two weeks, in helping out our friends at PilotEdge, I was part of a team that generated traffic for testing avionics equipment and the TCAS system for a design team. Before that, i was part of a team that was itself testing a newly designed simulator. back in February of 2018, as part of study worm at Embry Riddle University, there were many discussions around the use of distributed remote ops concepts that could help build safety scenarios in the use of drones. While all or most of these are concepts, it is very apparent that the combinatorial power of a simulation appliance and the network is phenomenal.

The internet of things is here. Pretty much any device can be provisioned with an IP address. As such, it can participate in a network. The simulator was an extraordinarily useful safety and proficiency device. Combining it into a network has brought out a series of new possibilities. Real-time weather generation, traffic scenario generation, communications testing are just a few of those advantages.

The ability for a piece of simulation hardware to talk to learning management systems and learning content management systems is a valuable opportunity. Taking it a step further. if the learning management system was adaptive, this would add a new dimension to pacing learning based on learner assimilation and learner type. Now with the use of ML, the generation of scenarios based on measures of central tendency have become easier. Content packaging using SCORM and/or IMS makes for standard scenario packages. A learning record store provides for persistence in student progress tracking. Progress dashboards and giving the learner a unified experience becomes very possible. There are many other such benefits.

Aggregation has been the sought after path for several years. Simulators have arrived at that point now.

CJ

Transfer of training and Fidelity requirements

~ cjois

The ability to transfer training is what makes simulation environments important. Mirroring reality becomes very important. This theory is associated with general principles of learning psychology stemming back to the early 1900s.

There is a correlation between the objectives of training and need for fidelity.There are different phases of flight and not all are equal in all respects. However, each one demands a different type aspect of fidelity to be modeled accurately. A x-wind trainer training for crab angle or slip on short final will require that the DOFs are well modeled. In cruise flight on autopilot at 30000 feet, DOF modeling is less important. Instrument scans, PPoS and fuel monitoring become extremely important. Hence instrument fidelity takes dominance.

Its well documented in human factors research that complacency and ‘falling behind the curve’ is a common issue in cruise flight. Aural warnings, the FMS and Autopilot will need to be really high fidelity to accurately model nav and fuel burn. Coming back into a terminal area, radio comms, traffic, congestion, weather modeling (mins) take dominance.

Hence in my opinion, fidelity is a function of training objective and in each phase of flight a different aspect of fidelity takes over. I don’t think there is ever a ‘low stress’ phase of flight.

CPJ

#FSX and #Win10 update

~ cjois

I deferred updating my primary PC on my simulator till the very last minute because I was very apprehensive about breaking something that as working really well. Of course, the last week of July came by soon and the deadline for Win 10 upgrade came up. With a ton of trepidation I finally hit the upgrade button!

After the upgrade was complete, I hesitantly turned all of the components of my sim. Surprisingly at first, everything seems to be working as before. That was not the case, though.

here are some of the fixes i had to go through and I am listing them below in case you have the similar issues.

FDS MCP G1 – Driver needed reinstall. Prosim needed to be configured again to use the correct COM port.

NVIDIA Settings – My PC lost all of its #FSX profile related #NVIDIA customizations. If you have a backup of the profile, you may be able to import it back.

FSX.CFG – Win10 registered NVIDIA #GTX card as a new entry in the FSX.CFG file. With that, FSX when started didn’t use the prior resolution, filtering and aliasing settings. I had to reset each of these settings to prior settings.

#UltimateTraffic2 – #UT2 would not start. It would ask for registration. When attempted to register, UT2 would come up with an “Invalid License File” notification. This is related to Windows Defender. Defender quarantines the license key file. Open Windows Defender and restore the Ultimate Traffic key file. Once this is complete, UT2 will run correctly. This short YouTube Video helped me resolve this issue.

With these changes, my simulator seems to be running normally. I did not need any changes to #ProSim737, #Aerosoft scenery or any other hardware drivers (which I have many of!).

Hope this helps.

CPJ