Flight Simulation Training Devices: Application, Classification, and Research

Abstract

Safe and efficient training using flight simulation training devices (FSTD) is one of the fundamental components of training in the commercial, military, and general aviation. When compared with the live training, the most significant benefits of ground trainers include improved safety and the reduced cost of a pilot training process. Flight simulation is a multidisciplinary subject that relies on several research disciplines which have a tendency to be investigated separately and in parallel with each other. This paper presents a comprehensive overview of the research within the FSTD domain with a motivation to highlight contributions from separate research topics from a general aspect, which is necessary as FSTD is a complex man–machine system. Application areas of FSTD usage are addressed, and the terminology used in the literature is discussed. Identification, classification, and overview of major research fields in the FSTD domain are presented. Specific characteristics of FSTD for fighter aircraft are discussed separately.

Full text

Vol.:(0123456789)
1 3
International Journal of Aeronautical and Space Sciences
https://doi.org/10.1007/s42405-021-00358-y
ORIGINAL PAPER
Flight Simulation Training Devices: Application, Classification,
andResearch
JelenaVidakovic1 · MihailoLazarevic2 · VladimirKvrgic3 · IvanaVasovicMaksimovic1 · AleksandarRakic1
Received: 12 May 2020 / Revised: 5 January 2021 / Accepted: 18 January 2021
© The Korean Society for Aeronautical & Space Sciences 2021
Abstract
Safe and efficient training using flight simulation training devices (FSTD) is one of the fundamental components of training
in the commercial, military, and general aviation. When compared with the live training, the most significant benefits of
ground trainers include improved safety and the reduced cost of a pilot training process. Flight simulation is a multidisci-
plinary subject that relies on several research disciplines which have a tendency to be investigated separately and in parallel
with each other. This paper presents a comprehensive overview of the research within the FSTD domain with a motivation
to highlight contributions from separate research topics from a general aspect, which is necessary as FSTD is a complex
man–machine system. Application areas of FSTD usage are addressed, and the terminology used in the literature is discussed.
Identification, classification, and overview of major research fields in the FSTD domain are presented. Specific characteristics
of FSTD for fighter aircraft are discussed separately.
Keywords Flight simulation· Training devices· Ground trainers· Fighter aircraft
1 Introduction
A pilot training process generally relies on a number of
learning methodologies and training aids used within three
learning concepts: (1) theoretical studies; (2) training in
flight simulation training devices (FSTD); (3) live train-
ing. The purpose of flight simulation is to reproduce (on
the ground) the behavior of an aircraft in flight as sensed
by the cockpit crew members, so that they can, by flying
the simulator, develop, and maintain the skills necessary to
safely and efficiently operate the real aircraft and prove their
proficiency to examiner [1].
Over the last forty years, flight simulation has made a
major contribution to flight safety and became essential for
the operation of civil airlines and military organizations
[2–4]. Training with FSTD reduces the number of training
hours needed in the air for a student to reach a defined pro-
ficiency level; thus, reducing high costs of operating and
maintenance of a fleet of aircraft [5]. Flying a real aircraft
require coordination with numerous other services (e.g., air
traffic control and maintenance), and need of appropriate
weather and visibility conditions, which is avoided with
the use of FSTD [2]. In this sense, FSTDs contribute to the
reduction of time of the making-ready-pilot process. From
an environmental point of view, ground trainers represent a
much favorable alternative to training in aircraft [2].
Even though the first commercially built pilot ground
trainers emerged at the beginning of the last century [2], the
real use of flight simulation started in the military during
World War II [1, 2, 6]. After World War II through the late
1950s, flight simulation branched out from the military into
commercial aviation [6]. The significant number of different
aircraft in service and under development in commercial
aviation prompted the design and deployment of flight simu-
lation training devices that attempted to replicate the char-
acteristics of specific aircraft. The breakthrough in the flight
simulation industry came with computer development in the
1980s [2, 7], and since then, a significant transition has been
made from live training to ground trainers-based training.
Improvements in flight simulation technology and the data
acquired from flight tests provided by aircraft manufacturers
* Jelena Vidakovic
jelena.vidakovic@li.rs
1 Lola Institute, Kneza Viseslava 70a, Belgrade, Serbia
2 Faculty ofMechanical Engineering, University ofBelgrade,
Kraljice Marije 16, Belgrade, Serbia
3 Institute Mihajlo Pupin (IMP), University ofBelgrade,
Volgina 15, Belgrade, Serbia

International Journal of Aeronautical and Space Sciences
1 3
(with detailed aerodynamic and engine models for different
regimes within the flight envelope), together with some reg-
ulations and practices established by the regulatory authori-
ties, were instrumental for the mentioned advancement [2].
They contributed to the broader acceptance of flight simula-
tion in the military, which in many countries happened only
in the last decade of the twentieth century [8].
Flight simulation training devices are currently used for
the training of a cockpit crew, maintenance personnel, and
command and supervisory staff [1, 9, 10]. Flight simulation
has made a significant impact in the systems engineering
field including human factors research [3, 11, 12]. FSTDs
are used for flight training program design and development
[13], and accident investigations [1, 14].
Flight simulation is a multidisciplinary subject that relies
on several research disciplines, such as sensory perceptions,
motion actuation, visual image generation, human factors,
etc. This research tends to be investigated separately and in
parallel with each other. Overview studies and meta-analysis
on a certain aspect of FSTDs, for example on the value of
motion cueing, can be found in the literature; however, there
are no recent general-point-of-view overviews. Furthermore,
an inquiry of the literature shows that there is inconsistency
regarding the applied terminology for flight simulation train-
ing devices, which may add to the confusion during analysis
and application of the available research, and that there is
very little published material on classification and general
application of FSTDs.
This paper provides a comprehensive overview of the
research within FSTD domain with a motivation to con-
nect and highlight contributions from separate research
topics from a general aspect, which is necessary as FSTD
is a complex man–machine system. To perform general
research overview, it is important to establish clear termi-
nology which has been found to depend on the application of
FSTDs. Two main areas of usage of FSTDs have been identi-
fied, and the terminology used in the literature is discussed.
An overview of research is presented, classified into two
major research topics. Identification, classification, and over-
view of major research fields in the FSTD domain are pre-
sented. Herein, we consider ground trainers for fixed-wing
aircraft. Specific characteristics of FSTD for fighter aircraft
are discussed separately. Visual presentation describing the
structure of the paper is given in Fig.1.
2 Application, Terminology,
andClassication
2.1 FSTD Application
Herein, we consider two main application areas of FSTD: (1)
training; and (2) system engineering (research). Graphical
representation of the major FSTD applications categories is
given in Fig.2, while the literature used in this Section is
systematically listed in Table1 according to areas of FSTD
usage.
Fig. 1 Visual layout of the paper structure
Fig. 2 FSTD application categories
Table 1 Application of FSTDs literature
Application References
Training
General training and licensing
UPRT
[1–3, 5, 9, 10, 13, 21, 22]
[16–20]
System engineering
Control research
Avionics design and validation
Aircraft stability
Human factors
Accident investigations
[1, 23–25]
[2, 23, 27]
[1, 26]
[5, 9, 11–13, 21, 28–32]
[1, 14]

International Journal of Aeronautical and Space Sciences
1 3
2.1.1 Training
Training is the basic purpose of FSTDs. FSTDs are used for
the training of a cockpit crew, maintenance personnel, [13,
15], and command and supervisory staff [1, 9, 10]. FSTDs
that provide duplication of actual equipment or systems are
employed for purposes like familiarization, acquiring proce-
dural and continuous control skills, learning part or whole-
task performance, and the practice of complete or segments
of missions [1, 5]. Simulators can recreate various locations,
weather (e.g., wind, turbulence, and visibility), equipment
failure [13], and provide training in extended flight enve-
lopes [16–20]. FSTDs are particularly suitable for skill
decay-preventing recurrent training [21]. Flight simulators
are used for the purposes of personnel selection and licens-
ing [1, 22]. Multi-crew pilot license training recommenda-
tions for FSTD-based training for four different phases of
pilot training according to ICAO are described in [22].
2.1.2 System Engineering (Research)
Flight simulator training devices have been used for system
engineering research in aviation for more than 30years [23].
An overview of research shows that the usage of FSTDs in
the system engineering field can be classified into two major
groups: (1) aircraft and equipment design and validation and
(2) human factors in aviation.
Usage of FSTDs for aircraft and equipment design and
validation involves control research [1, 23–25], aircraft
stability [1, 26], and avionics design and validation (e.g.
research prototypes of hazard sensors, weather condition
information processing systems, display concepts, etc.) [2,
23, 27].
Most of the human factor research in aviation is con-
ducted in FSTDs due to requirements for sophisticated
experimental designs and instrumentation, and high cost of
flights. Much of the research deals with cockpit or display
variables, which may not require actual flight [13]. Human
factors studies performed in FSTDs are given in [5, 9,
11–13, 21, 28–32].
2.2 Classification andTerminology
The term flight simulator (FS) is often used amongst the
researchers, various engineering and software specialist,
and enthusiasts. Survey of the literature shows that there
is inconsistency in the use of the term “flight simulator”,
as it may indicate a device that provides equipment, and
both visual and motion cues [33, 34], or a device that pro-
vides equipment and visual cues [2, 13, 32, 35–37], or a
device that provides only equipment cues [1]. Significant
software on the verge of games is labeled as a flight simula-
tor, and even though many of this software is marketed as
entertainment, they are useful within some aspects of abini-
tio training [38, 39]. Inconsistency regarding the applied
terminology for FSTDs may add to the confusion during
analysis and application of the available research on the
flight simulation technology. In this section we address this
important issue.
When considering applied terminology within flight
simulation domain in the literature, it has to be taken into
consideration that, according to their main purpose there are
two categories of devices that simulate flight conditions on
the ground: (1) FSTDs used in training and (2) FSTDs used
in research, including high-end, advanced FSTDs [1, 40].
As a result of the wide acceptance of flight simulation
in training, classifications and terminology of FSTDs used
in training are strictly defined by regulatory authorities to
be used by certification bodies responsible for evaluation,
and qualification of FSTDs. International and national agen-
cies and association bodies, such as ICAO (international),
EASA (Europe), FAA (USA) [41], and others [42] define
strict guidance on how to qualify FSTDs. European Avia-
tion Safety Agency (EASA) defines flight simulation train-
ing device as any type of device in which flight conditions
are simulated on the ground, including (1) flight simulators
(FS); (2) flight training devices (FTD); (3) flight and naviga-
tion procedures trainers (FNPT); and (4) basic instrument
training devices (BITD) [43]. Other training device (OTD) is
defined as a training aid other than an FSTD which provides
for training where a complete flight deck/cockpit environ-
ment is not necessary [43]. In Table2, the interpretation of
FSTDs approved by EASA is given. An FFS does not have
to replicate all the physical aspects of flight but only com-
ply with minimum requirements approved by the qualifying
authority. In [43], the levels of FFS are A, B, C, D (from
lowest to the highest level, respectively), with defined mini-
mum requirements for visual, sound, and motion simulation
systems that also include flight controls (response to control
inputs), buffet simulation effects, wind shear, etc. The cur-
rent commercial FFS is mostly based on Stewart platform
presented in Fig.3. Upset prevention and recovery training
(UPRT) are required for 6DoF C and D levels of FFS in [43].
Presented classifications are not obligatory for FSTDs
used in research, and potentially may not be applicable for
a new type of FSTD. Some studies acknowledge three types
of FSTDs used in research: FFS, personal computer aviation
training devices (PCATD), and part-task trainers [1, 13].
Some motion platforms, such as high-end centrifuge-based
motion simulators, dedicated spatial disorientation trainers,
etc. that do not imply pilot control feedback would likely be
classified within FNPT category of [43]. However, FSTDs
categories [41–43] are created primarily having in mind the
devices to train pilot handling skills or pilot and crew aircraft
system operations, and are not intrinsic for the mentioned
FSTDs that do not involve pilot control feedback.

International Journal of Aeronautical and Space Sciences
1 3
3 An Overview ofResearch inFlight
Simulation
Underlying technologies of flight simulation include math-
ematical modeling, real-time computing, motion actuation,
visual image generation systems, projection systems for vis-
ual cueing, etc. [2]. The correct mathematical model and/or
data on the characteristic features of the aircraft that together
describe the aircraft behavior in response to pilot inputs and
external disturbances may be obtained from theoretical anal-
ysis, wind tunnel measurements, and flight tests [1].
Research problems in FSTDs are usually investigated
separately, and there are few older date general point of view
studies. From an inquiry of the literature, we notice that
most of the research regarding flight simulation achievement
can be classified into two categories: (1) effective usage of
flight simulation technology; and (2) improvements in cue-
ing fidelity. Identified major research categories are graphi-
cally described in Fig.4 with a motivation to highlight and
connect contributions from separate research topics from a
general aspect. Literature used in this section is presented in
Table3, according to identified research areas.
3.1 Effective Usage ofFlight Simulation Technology
Despite the recent advances in flight simulation technology,
when considering the practical implications of integrating
simulation-based training, positive outcomes for time and
cost-effectiveness, factors that influence the overall justifica-
tion of FSTD use, are not always guaranteed [45]. Optimiz-
ing flight curricula to capitalize on the strengths of the avail-
able FSTDs of lower and higher fidelity is critical to flight
training [10]. There is considerable debate regarding the
influence of simulator fidelity on the effectiveness of FSTD
usage, particularly regarding the impact of motion [34, 40].
Table 2 Interpretation of FSTD classification requirements according to EASA [43]
FSTD type Flight deck/cockpit environment Simulation capabilities Equipment and software specifications Visual system Force cue-
ing motion
system
FFS A full-size replica of a flight deck/
cockpit of a specific type or make,
model and series of aircraft
Represents the airplane in ground and flight
operations
Includes assemblage of all equipment.
Includes computer software programs
Required to provide an out
of the flight deck/cockpit
view
Required
FTD A full-size replica of a specific
aircraft type’s instruments, equip-
ment, panels and controls
Represents the aircraft in ground and flight
conditions to the extent of the systems
installed in the device
Includes assemblage of equipment. Includes
computer software programs
Not required Not required
FNPT The flight deck/cockpit environment Represents an aircraft or class of aeroplane in
flight operations to the extent that the sys-
tems appear to function as in an aircraft
Includes assemblage of equipment. Includes
computer software programs
Not required Not required
BITD The student pilot’s station Provides at least the procedural aspects of
instrument flight for a class of aeroplanes
Not specified, likely use of screen-based
instrument panels and spring loaded flight
controls
Not required Not required
Fig. 3 Stewart platform [44] with universal and spherical joints

International Journal of Aeronautical and Space Sciences
1 3
Studies regarding simulator fidelity are inconclusive and, at
times, seemingly contradictory, with many asserting fidelity
does not affect training transfer while others affirm impacts
[34]. From a literature overview, it can be concluded that
the value of fidelity for pilot training depends on the level
of pilot proficiency [10, 34, 39, 45–47].
There is a discrepancy in the nature of skills being
learned during abinitio compared to more advanced phases
of pilot training [39, 45]. It is said that the usage of high-
fidelity FSTD is cost-effective for advanced stages of com-
mercial and military pilot training, but to a lesser extent for
early abinitio phases [45]. Furthermore, it is argued that the
fidelity of a real-world flight environment may even detract
from, rather than enhance, the performance of a novice pilot
[48]. Determination of performance variables through iden-
tification and examination of aspects of the set task that are
critical for improving skills is necessary to establish efficient
training programs [49]. The inclusion of learning theories
is of great importance within general efforts to increase the
effectiveness of flight training programs, including FSTD-
based training, some of this research is presented in [5, 13,
21, 31, 36, 47, 48, 50]. The extent to which trained behav-
ior transfers between different environments (e.g., an FSTD
or an aircraft) is mostly influenced by the environmental
dependency of the applied skills [36]. Determination and
understanding of the nature of these environmental depend-
encies are essential for the effective use of flight simulation
technology. For this purpose, transfer-of-training experi-
ments (ToT) can be used. ToT is one of the few available
methods for direct evaluation of the effectiveness of simula-
tor-based training [36, 51]. To evaluate simulator effective-
ness, i.e., to calculate the amount of transfer, several formu-
las for transfer effectiveness ratio (TER) are developed [50,
52]. The variables that can be used to quantify the transfer
of training are for example the number of training sessions,
the total time necessary for training to criterion, the cost of
using a simulator versus the cost of actual flight, counts of
errors made while performing a task, etc. [52]. It is said that
the use of flight simulators is cost-effective if the TER is 0.2
or greater [15]. The incremental transfer effectiveness ratio
(ITER) is used as the indicator of the transfer effectiveness
of successive increments of training in the ground trainer
[50].
Besides the training purpose of FSTD, fidelity is an
important concept to be taken into account for the system
engineering applications of FSTD described in Sect.2.2,
Fig. 4 FSTD research classification
Table 3 Research on the topic
of flight simulation Research topic References
General review papers [1, 2, 5, 7]
The influence of cueing fidelity to effectiveness of training
Vision
Motion
[7, 10, 34, 39, 49]
[7, 34–37, 46, 49, 51–58]
Integration of flight simulation into training [10, 35, 39, 45]
Sensory perception
Role of human factors on integration of cues in FSTD
[1, 25, 63, 65–72]
[60, 73]
Washout
Objective motion cueing fidelity analysis
Motion cueing for UPRT
Cybernetic approach
[64, 66, 86, 87, 89, 90]
[91, 93]
[16–20, 82–85]
[20, 25, 62, 88]

International Journal of Aeronautical and Space Sciences
1 3
where it is necessary to select the right level of simulation
fidelity. Using high-fidelity FSTDs, the costs increase expo-
nentially, whereas the level of flexibility, i.e., the ease of
changing, integrating, and testing a prototype decreases [32].
3.1.1 Evaluation ofMotion Cueing Effectiveness
Motion cueing effectiveness is a major research interest
within investigations in the domain of effective usage of
flight simulation technology. Importance of platform motion
in FSTDs has been debated in the literature for more than
half of the century [35–37, 46, 49, 51, 53–58]. The main
drive for the studies regarding motion effectiveness in flight
simulation is the need for reduction of cost. Also, the use
of a motion platform with poor motion cues can produce
simulator sickness, and produce negative transfer of training
[59]. Some researchers argue that there are still no compre-
hensive guidelines for determining where and why motion
simulation is cost or time effective in pilot training [45, 49].
Nevertheless, regulatory training and civil licensing authori-
ties require motion (FFS) to be used for the advanced phases
of training [22]. Research shows that the presence of motion
cues adds to pilot acceptance and subjective preference for
motion, even while lacking the concomitant objective evi-
dence [46]. Motion is valuable for some aspects of system
engineering application of FSTDs, and for pilot licensing
using FSTDs [35, 60].
The value of motion cueing in training is said to be appli-
cation specific, and the meta-analytic approach proved to be
essential for determining the value of platform motion [35].
In motion simulators, two types of motion are differenti-
ated: motion related to pilot maneuvering (associated with
control actions, i.e., motion feedback), and motion related to
environmental changes or disturbance (cues associated with
motion due to wind shears, turbulence, or engine failure)
[35, 56]. The presence of disturbance cues is found impor-
tant for the transfer of training in [35, 49, 54, 56, 58]. The
bulk of studies [37, 49, 51, 53, 55–57] found that maneuver-
ing motion does not necessarily contribute to the transfer of
training for fixed-wing aircraft. In other research, it is found
that motion feedback achieves the positive or bigger transfer
of training for novices [35, 51, 54], and that is required for
effective initial simulator-based training of skill-based man-
ual control [36]. In [46], it is found that for high-altitude stall
recovery and overbank recovery, motion improved results. In
[61], it is found that physical motion cues are immediately
useful for the disambiguation of the visual signal.
It is argued that one of the reasons for the continuing
controversy regarding platform motion value is the issue of
collecting convincing and generalizable evidence regarding
training effectiveness, which requires reliable and quantita-
tive data regarding trainees’ developing skills [62]. Besides,
numerous variables might influence the effectiveness of
motion cueing and impact on the observed effects, includ-
ing the presence and quality of the visual display, temporal
synchronization between motion and visuals, the quality
of auditory cues, the dynamic model and type of aircraft,
degrees of freedom of the motion system, duration and type
of training, measurement equipment used, calibration set-
tings and the motion cueing algorithm (MCA) [35, 59, 63,
64].
3.2 Improvements inCueing Fidelity
Regarding improvements in cueing fidelity, two major
research fields can be identified: (1) sensory perceptions
and (2) improvement of motion cueing systems. Besides
advanced high-end FSTDs development, a survey of recent
research in the field of improvement of motion cueing sys-
tems points to the two current research interests: achieve-
ment of UPRT, and MCA development and evaluation.
1. Sensory perceptions
In piloted flight, vision is the primary sense used
within perception of motion. The non-visual (inertial)
human sensors that enable the simulation of motion in a
limited space of flight simulators are (1) the vestibular
system (semi-circular canals and the otoliths); (2) pres-
sure sensors (surface tactile receptors and deep pressure
sensors); and (3) proprioceptive and kinesthetic sensors
[1]. Auditory signals, such as engine sound or wind
noise also provide important indirect information on
self-motion that pilots use, especially during simulation
based on the vection [65]. The visual system of a flight
simulator is particularly effective in simulating motion
patterns that are relatively constant [63, 66]. Changing
motion patterns are the best perceived by vestibular
inner ear system and other proprioceptors that are spe-
cifically tuned to acceleration [67].
Flight simulators try to simulate motion trajectories
which are considerably larger than the actual range of
the physical simulator device [63, 66]. Larger range of
movement allows for more accurate acceleration cue-
ing; however, increasing the number of degrees of free-
dom and enlarging the movement range of the simula-
tor raises the costs of the device considerably [1, 66].
By using the investigations of the perception of self-
motion, researchers are oriented to the development of
techniques (e.g. acceleration onset cueing and angular
displacement of the platform to trade the gravity vector
for a perception of acceleration) to mimic the accelera-
tions that act on the body during self-motion in limited
motion space of ground trainers using human vestibular,
tactile, and visual systems combined to provide a con-
vincing perceptual illusion of motion [1, 35, 66, 67].
The quality of the motion cueing algorithm is directly

International Journal of Aeronautical and Space Sciences
1 3
dependent on the implemented perception models for
self-motion, some of these models can be find in [25,
65, 66, 68–70]. To avoid visual-motion cueing conflicts,
investigation of the perception of coherence zones in
flight simulation is performed, [71] and the influence of
motion perception thresholds is investigated [71, 72].
The effects of platform motion on the pilots’ perception
of specific aircraft maneuvers are investigated [69]. The
recent research on the role of human factors on the suc-
cess of integration of visual and inertial cues in FSTDs
is given in [60, 73]. The modeling of skill-based pilot
control behavior, a so called cybernetic approach [20,
25, 62] received attention lately.
2. Improvement of motion cueing systems
Conventional flight simulators are designed as a 6 DoF
parallel manipulator, also known as hexapod, or Stewart
platform [35, 44, 74]. Hexapods make up to 80% in training
devices that the FAA qualifies [46]. Besides Stewart plat-
form, FSTD with motion platforms of different kinematic
configurations have been developed. Notable examples
include: (1) centrifuge motion simulator (CMS), also known
as human centrifuge, which provides planetary motion [75,
76]; (2) Vertical motion simulator, which is uncoupled 6DoF
mechanism that consists of 4DoF platform mounted on a
2DoF large-amplitude platform [46, 77]; (3) Desdemona
simulator, a device that combines a centrifuge design with
six serial DoFs, and has the capability to translate along the
centrifuge radius, and capability to move up and down in
heave [78, 79]. These devices are capable of overcoming
some of the limitations of the hexapod motion platform;
however, the costs to procure and maintain such systems are
high so that the hexapods are still the most common choice
for training and evaluation of airline pilots requiring “full”
motion simulation [37, 80].
One of the significant factors that lead to loss of con-
trol (LOC) in flight, which has and continues to remain the
number one cause of passenger fatalities [18, 81], is the
entry of an aircraft into an upset, a situation in which an
aircraft unintentionally exceeds the parameters normally
experienced in line operations or training [18]. Generally,
it includes inappropriate pitch attitudes (greater than 25°
nose up or 10° nose down), bank angles (greater than 45°),
by flying in airspeed inappropriate for the flight conditions
[18]. One of the most important upsets to be recovered from
is stall. When exercising UPRT using the commercially
operated FFS-conventional hexapod type motion platform,
it has to be taken into account that (1) the motion envelope
of hexapod platforms is limited so that they are unable to
replicate the certain motion cues that occur during upsets
[16, 17] and sustained G-loads that accompany certain upset
recoveries [18]; (2) majority of the upsets and consequent
recovery events exceed the normal flight envelope, where
the aerodynamic model has not been developed or validated
[16, 18, 82]. Recent efforts to attain expanded aerodynamic
envelope for flight simulation in the realm of UPRT for air-
line pilots are presented in [82–84]. Motion cueing strategies
for stall recovery training in commercial transport simula-
tors are presented in [17, 18, 20, 82, 85]. The study from
2013 [19] identified training tasks involved in UPRT which
require an upgrade of, or addition to existing FSTD, or an
extension of the FSTD’s aerodynamic model, whereas it is
suggested that some tasks that involve spatial disorientation
and G-load management should be performed on an air-
plane, or, alternatively, using special ground trainers.
MCA, commonly called the washout, is employed to
provide the best possible motion cues within the capabil-
ity of the simulator throughout the flight envelope [59, 86].
MCA modifies the simulated aircraft’s motion into simu-
lator motions that fit inside the simulator’s motion space,
and it incorporates filtering necessary to overcome the lim-
ited motion envelope of the motion base [86, 87], such as
frequency-independent scaling to reduce the overall mag-
nitude of the cued simulator motion, and high-pass filter-
ing to attenuate the low-frequency motion that is especially
difficult to replicate [88]. Within the design of a simulator
motion filter, human motion perception and control models
play crucial role [69]. On the other hand, pilots’ percep-
tion of the motion cues depends significantly on how well
MCA has been adjusted [59]. Different control strategies
have been employed within washout design, such as clas-
sical washout (still widely used in commercial simulators
[18]), optimal control, coordinated adaptive, model predic-
tive control, etc. [64, 78, 80, 86, 87, 89, 90]. MCA tuning
and evaluation [80, 87], up until recently, has been primarily
based on the subjective judgment of experienced pilots [59,
80]. Objective motion cueing test (OMCT), which compares
simulator response (including MCA, transport delays, and
motion platform dynamics) to the aircraft input signal, is
proposed recently and adopted as a part of the latest ICAO
criteria for qualification of FSTD [91–93]. The OMCT was
recentlyadopted by the FAA, albeit without the fidelity cri-
teria for the OMCT [93]. Recent use of cybernetic approach
in validation of motion cueing algorithms developed for stall
recovery training is presented in [20], and for the purpose
of objective evaluation of flight simulator motion cueing
fidelity is given in [88].
4 Flight Simulation Training Devices
forFighter Aircraft
The expensive process of military pilot training is pre-
ceded by medical examinations, aptitude testing, and flight
screening [94]. The main goal of the fighter pilot training
process is to produce a combat-ready pilot at an affordable

International Journal of Aeronautical and Space Sciences
1 3
cost within the shortest possible period. Owing to the high
cost of combat aircraft procurement and maintenance, air
forces worldwide struggle to preserve the combat readi-
ness of their pilots. Maintaining a high level of readiness
with smaller numbers of aircraft requires the efficient use
of alternative means of training, such as FSTDs [95]. In the
military, FSTDs are used for aircrewtraining andtesting,
and for airplane maintenance tasks [1, 96, 97]. Regarding
training, FSTD serves several functions: it provides cost-
effective training aid with reduced risk to flight crews, lesser
environmental impact, and better preservation of strategic
and tactical secrecy.
Military use of FSTD starts as a part of abinitio pilot
training and continues with advanced training, including
type conversion, and training for specific tasks like air-to-
air refueling, airborne surveillance, landing on a ship, land-
ing in woods/mountain, air-to-air combat [1, 9, 97]. Besides
avoiding high costs of operating and maintenance of aircraft,
the cost of deployment of expensive weapons on training
ranges is reduced. Today, the smallest unit that performs
air combat missions is the two-ship section (two aircraft),
which makes team coordination to be an essential part of
air combat [95]. For fighter aircraft pilots, FSTDs provides
an effective training aid for crew cooperation [2]. A mis-
sion rehearsal capability is introduced within FSTD, with
the flight crews enabled to operate in multi-aircraft forma-
tions [2, 98]. FSTDs have been used for the development
of aircraft avionics, including displays, flight management
systems (FMS), radars, warning systems and monitoring
systems [2].
While the commercial pilot’s job is to minimize turbu-
lence, overcome visual distractions and system failures, and
limit the effects on passengers and cargo, the combat-ready
fighter or strike fighter pilot has an entirely different mission:
to outlast his/her opponent in an often hostile and foreign
environment [81]. Defending and employing an aircraft,
especially in combat, requires high pilot skill levels [49].
Modern combat aircraft pilots are exposed to extreme flight
conditions in which the pilot’s ability to control the aircraft
is reduced. The scope of the EASA civilian FSTD is rela-
tively limited as it includes take off, land and cruise [99].
The technology of civil flight training is not appropriate for
all military training programmers [7]. In particular, (1) the
pilot of a fighter aircraft is not constrained to a forward-
looking windscreen, and (2) the sustained acceleration levels
that occur during turning maneuvers cannot be replicated in
hexapods [7, 74].
Hexapod devices cannot achieve sustained high-G load
that fighter aircraft pilots are subjected to (accelerations dur-
ing flight go up to 9g [76, 81]); however they are particularly
suitable for simulation of flight conditions with low G-load
values such as landing simulation where pilot response is
critically dependent on the fidelity of the visual and motion
cues provided by the simulator [100]. High G-load effects on
pilots include G-LOC (G-force induced Loss Of Conscious-
ness), neck injuries, vibration effects, reach envelope reduc-
tion, vestibular illusions, etc. [101]. Combat aircraft are lim-
ited in terms of the maximum level of G-load that they may
be exposed to for a specified period and must be returned for
maintenance if this maximum level is exceeded [75]. CMS
is a motion platform typically employed in the military for
simulation of high-G accelerations (transient and sustained).
It has one or more DoFs, and if it’s qualified by certification
bodies, it can fall into the category of FFS [108] or FNPT
[99], depending on the flight deck/cockpit environment. The
drawbacks regarding CMS usage are related to the high cost
of the procurement, usage and maintenance of the device
due to its large size. CMS requires very powerful motors to
achieve fast variation in acceleration in the gondola [100,
102–104]. For any large FSTD, particular attention has to
be given to match the lags of the motion platform and the
visual system (including projection), to avoid the occurrence
of simulator sickness [7], and for this reason, visual cues are
often minimal within CMS.
Many military organizations in the past have opted to
use fixed-base simulators with a wide field of view visual
displays (e.g., hemispherical domes) [7, 49, 79]. Various
militaries make use of G-seats, or dynamic seats [55, 58],
where a pilot is subject to haptic pressure applied by mov-
ing the seat pan and sides to simulate forces on the body
during high-G maneuvers [7, 74, 105, 106]. These cues are
often combined with visual cues, and also with vibrations
to simulate high-frequency accelerations [7].
Dynamic flight simulator (DFS) is defined to be a cen-
trifuge operated as a flight simulator, i.e. driven by pilot
commands in the cabin in response to a perceived flight con-
dition [74, 100]. The critical problem in the achievement of
dynamic flight simulation is to obtain a required response
time of the simulator to a given change in control by the pilot
with a massive device and to avoid degrading the pilot’s per-
ception of flight caused by fast acceleration change artifacts
and vestibular Coriolis cross-coupling effect in centrifuges
[100, 107]. Flight simulation training devices that, through
the integration of a high-performance planetary motion cen-
trifuge with a gimbaled, flyable, cockpit module, are capable
of replicating G forces experienced in flight and achieving
flight simulation are also called continuous G devices (CGD)
in the literature [79]. Besides the potential for fully recreat-
ing flight environment for fighter aircraft, CGD can provide
UPRT training for civil FSTD operators. In the study [79],
existing examples of CGD are identified to be Desdemona
simulator [78, 79], GYROLAB series of spatial disorien-
tation trainers [79, 108], and Authentic Tactical Fighting
System (ATFS)-400 [79, 108].
Besides high values of sustained and transient G-load,
another major issue for fighter pilots is spatial disorientation

International Journal of Aeronautical and Space Sciences
1 3
(SD). SD is one of the main causes of fatal accidents in the
military (it accounts for 6–32% of major and 15–69% of fatal
military accidents [109]). As a measure of preventive against
the SD, research and regulatory bodies suggest training of
flight crew in aircraft, as well as in motion simulators to
procedures and techniques that enable them to recognize and
solve problems related to SD during the flight in civil and
military aircraft [4, 110–112]. Training in motion simula-
tors is dedicated to teaching the pilot to recognize unusual
flight orientations, to train the pilot to adapt to them and to
persuade the pilot to believe in the aircraft instruments for
orientation, and not into his senses [111]. SD training for
fighter pilots is regularly used in military organizations [113]
however, there are very few standardized training procedures
for SD simulation and training [113]. The reason for this is
perhaps due to the fact that the environment in which SD is
likely to occur is difficult to define [113]. FSTD used for SD
training in military and civil aviation vary considerably from
small to large, from pure SD demonstrators, to full-flight
simulators [4, 79, 113].
Transfer-of-training studies are rare in the military
domain [53, 96, 97, 114–116]. Research on the effective-
ness of the flight simulation for fighter aircraft pilots is pre-
sented in [6, 9, 15, 49, 96, 97]. Recent efforts to provide
pilot behavior models is given in [107]. Research into the
relative training benefits of fixed-base platforms, synergis-
tic platforms, and G-seats is given in [37, 53, 55, 58, 105],
however, a little research is presented lately [7]. Literature
used in this section is presented in Table4.
5 Conclusion
Over the last forty years, flight simulation has made a major
contribution to flight safety and became critical to the opera-
tion of civil airlines and military organizations for provid-
ing a cost-reduced pilot training process. Inthis paper, a
comprehensive overview of the available research within the
FSTD domainis presented. Training, and system engineer-
ing (research), recognized within this study as two major
application areas of FSTDs, are addressed. Terminology and
classification regarding FSTDs used for training and FSTDs
used for research are discussed. An overview of research
is given, classified into two categories: effective usage of
flight simulation technology, and improvements in cueing
fidelity. Motion cueing effectiveness is discussed sepa-
rately. Regarding improvements in cueing fidelity, a survey
of research identified two major research domains: sensory
perceptions, and improvement of motion cueing systems. An
investigation on the literature in the field of improvement of
motion cueing systems, besides advanced high-end FSTDs
development, points to the two current research interests:
achievement of upset prevention and recovery training, and
motion cueing algorithms development and evaluation. The
specificities of flight simulation for fighter aircraft are dis-
cussed separately, with benefits and drawbacks of typically
used motion platforms addressed. To achieve effective flight
simulation, simulation designers have to determine how
much and what kind of simulation fidelity is needed taking
training context into account, along with establishing and
implementing an optimized training syllabus to capitalize
on the strengths of the available FSTDs of lower and higher
fidelity.
Acknowledgements This research has been supported by the research
Grants of the Serbian Ministry of Education, Science and Technologi-
cal Development.
Funding This research has been supported by the research grants
of the Serbian Ministry of Education, Science and Technological
Development.
Availability of data and material Not applicable.
Code availability Not applicable.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
References
1. Baarspul M (1990) A review of flight simulation techniques. Prog
Aerosp Sci 27(1):1–120
2. Allerton DJ (2010) The impact of flight simulation in aerospace.
Aeronaut J 114(1162):747–756
3. Chung W (2000) A review of approaches to determine the effec-
tiveness of ground-based flight simulation. In: Proceedings of
AIAA modeling and simulation technologies conference, p 4928
4. Bles W (2008) Spatial disorientation training demonstration
and avoidance. Technical Report No. RTO-TR-HFM-118. Nato
Research and technology organization, Neuilly-sur-Seine
Table 4 FSTD for fighter
aircraft literature Flight simulation for fighter aircraft effectiveness (value of motion,
vision fidelity, ToT experiments)
[6, 9, 15, 49, 53, 96–98, 114–116]
G-load simulation in centrifuges [74–76, 81, 102, 103, 107]
G-seats/dynamic seat [7, 37, 55, 58, 105, 106]
Dynamic flight simulation and CGD [74, 79, 100, 104, 112]
Spatial disorientation training [4, 79, 109–113]

International Journal of Aeronautical and Space Sciences
1 3
5. Smode AF, Hall ER, Meyer DE (1966) An assessment of research
relevant to pilot training, vol II. Technical Report No. AMRL-
TR-66-196, Biotechnology Inc, Arlington
6. Schreiber B, Bennett W, Colegrove C etal (2009) Evaluating
pilot performance. In: Anders EK (ed) Development of profes-
sional expertise: toward measurement of expert performance and
design of optimal learning environments, 1st edn. Cambridge
University Press, Cambridge, pp 247–270
7. Allerton D (2009) Principles of flight simulation. Wiley,
Chichester
8. Page RL (2000) Brief history of flight simulation. In: Proceed-
ings of SimTecT, pp 11–17
9. Johnston PJ, Catano VM (2013) Investigating the validity of pre-
vious flying experience, both actual and simulated, in predicting
initial and advanced military pilot training performance. Int J
Aviat Psychol 23(3):227–244
10. Macchiarella ND, Arban PK, Doherty SM (2006) Transfer of
training from flight training devices to flight for ab-initio pilots.
Int J Appl Aviat Stud 6(2):299–314
11. Boril J, Jirgl M, Jalovecky R (2016) Use of flight simulators in
analyzing pilot behavior. In: Proceedings of IFIP AIAI 2016, pp
255–263
12. Stroosma O, Van Paassen MM, Mulder M (2003) Using the
SIMONA research simulator for human–machine interaction
research. In: Proceedings of AIAA modeling and simulation
technologies conference and exhibit, p 5525
13. Wise JA, Hopkin VD, Garland DJ (2016) Handbook of aviation
human factors. CRC Press, Boca Raton
14. Air Accidents Investigation Branch (2016) Flight simulators and
air accident investigation. Annu Saf Rev
15. Fletcher JD, Chatelier PR (2000) An overview of military train-
ing. Technical Report, Institute for defense analyses, Alexandria
Va
16. Fucke L, Biryukov V, Grigoriev M etal (2010) Developing sce-
narios for research into upset recovery simulation. In: Proceed-
ings of AIAA modeling and simulation technologies conference,
p 7794
17. Zaichik LE, Yashin YP, Desyatnik PA (2012) Some aspects of
moving-base simulation of upset recovering maneuver. In: Pro-
ceedings of 28th congress of ICAS
18. Nooij SAE, Wentink M, Smaili H, Zaichik L, Groen EL (2017)
Motion simulation of transport aircraft in extended envelopes:
test pilot assessment. J Guid Control Dyn 40(4):776–788
19. Field J, Shikany D (2013) Simulator fidelity requirements for
upset prevention and recovery training. Technical Report NLR-
TP-2013-410, National Aerospace Laboratory (NLR).
20. Zaal P (2019) Motion cueing for stall recovery training in com-
mercial transport simulators. In: Proceedings of AIAA Scitech
2019 Forum
21. Childs JM, Spears W (1986) Flight-skill decay and recurrent
training. Percept Mot Skills 62:235–242
22. Wikander R, Dahlstrom N (2016) The multi crew pilot licence:
revolution, evolution or not even a solution? A review and analy-
sis of the emergence, current situation and future of the multi-
crew pilot licence (MPL). Lund University
23. Ulbig P, Müller D, Torens C etal (2019) Flight simulator-based
verification for model-based avionics applications on multi-core
targets. In: Proceedings of AIAA Scitech 2019 Forum, p 1976
24. Schreiter K, Müller S, Luckner R, Manzey D (2019) A flight
simulator study of an energy control system for manual flight.
IEEE Trans Hum Mach Syst 49(6):672–683
25. Lone M, Cooke A (2014) Review of pilot models used in aircraft
flight dynamics. Aerosp Sci Technol 34:55–74
26. Peterson T, Grant P (2011) Handling qualities of a blended
wing body aircraft. In: Proceedings of AIAA atmospheric flight
mechanics conference, p 6542
27. Daniels TS, Schaffner PR, Evans ET etal (2012) Creating a
realistic weather environment for motion-based piloted flight
simulation. In: Proceedings of IEEE/AIAA 31st digital avion-
ics systems conference, p. 2E5–1 IEEE.
28. Paces P, Jalovecky R, Blasch E, Stanek J (2015) Pilot control-
ler design using the CTU flight simulator for shared situation
awareness. In: Proceedings of 34th AIAA DASC, p 3E3-1
29. Allen JG, MacNaughton P, Cedeno-Laurent JG etal (2019)
Airplane pilot flight performance on 21 maneuvers in a flight
simulator under varying carbon dioxide concentrations. J Expo
Sci Environ Epidemiol 29(4):457–468
30. McClernon CK, McCauley ME, O’Connor PE, Warm JS (2011)
Stress training improves performance during a stressful flight.
Hum Factors 53(3):207–218
31. Rhind DJA, Head T (2004) The benefits of implicit learn-
ing when training ab-initio pilots. Int J Appl Aviat Stud
4(2):153–170
32. Oberhauser M, Dreyer D (2017) A virtual reality flight simu-
lator for human factors engineering. Cognit Technol Work
19(2–3):263–277
33. Dourado AO, Martin CA (2013) New concept of dynamic flight
simulator, part I. Aerosp Sci Technol 30(1):79–82
34. Myers PL, Starr AW, Mullins K (2018) Flight simulator fidelity,
training transfer, and the role of instructors in optimizing learn-
ing. Int J Aviat Aeronaut Aerosp 5(1):6
35. De Winter JCF, Dodou D, Mulder M (2012) Training effective-
ness of whole body flight simulator motion: a comprehensive
meta-analysis. Int J Aviat Psychol 22(2):164–183
36. Pool DM, Harder GA, Van Paassen MM (2016) Effects of simu-
lator motion feedback on training of skill-based control behavior.
J Guid Control Dyn 39(4):889–902
37. Burki-Cohen J, Sparko A, Go T (2007) Training value of a fixed-
base flight simulator with a dynamic seat. In: Proceedings of
AIAA modeling and simulation technologies conference and
exhibit, p 6564
38. Koonce JM, Bramble WJ (1998) Personal computer-based flight
training devices. Int J Aviat Psychol 8(3):277–292
39. Reweti S, Gilbey A, Jeffrey L (2017) Efficacy of low-cost
PC-based aviation training devices. J Inf Technol Educ-Res
16:127–142
40. Rehmann AJ, Mitman RD, Reynolds MC (1995) A handbook of
flight simulation fidelity requirements for human factors research.
Technical Report No. DOT/FAA/CT-TN95/46, Crew System
Ergonomics Information Analysis Center Wright-Patterson AFB
OH
41. 14 CFR FAR Part 60, Requirements for the evaluation, qualifica-
tion and maintenance of flight simulation training devices. https
://www.faa.gov/about /initi ative s/nsp/media /14cfr 60_searc hable
_versi on.pdf. Accessed 05 Mar 2020
42. Koblen I, Kovácová J (2012) Selected information on flight sim-
ulators-main requirements, categories and their development,
production and using for flight crew training in the both Slovak
Republic and Czech Republic conditions. Incas Bull 4(3):73
43. European Aviation Safety Agency (2018) Certification specifi-
cations for aeroplane flight simulation training devices, Issue
2. https ://www.easa.europ a.eu/sites /defau lt/files /dfu/CS-
FSTD%28A%29%20%E2%80%94%20Iss ue%202.pdf. Accessed
05 Mar 2020
44. Stewart D (1965) A platform with six degrees of freedom. Proc
Inst Mech Eng 180(1):371–386
45. McLean GMT, Lambeth S, Mavin T (2016) The use of simula-
tion in abinitio pilot training. Int J Aviat Psychol 26(1–2):36–45
46. Zaal PMT, Schroeder JA, Chung WW (2015) Transfer of training
on the vertical motion simulator. J Aircr 52(6):1971–1984
47. Dahlström N (2008) Pilot training in our time-use of flight train-
ing devices and simulators. Aviat 12(1):22–27

International Journal of Aeronautical and Space Sciences
1 3
48. Noble C (2002) The relationship between fidelity and learning
in aviation training and assessment. J Air Transp 7(3):33–54
49. Walker DR (2015) The impact of training context on perfor-
mance in simulator-based aviation training. Proc MODSIM
World 2015(11):2
50. Roscoe SN (1971) Incremental transfer effectiveness. Hum
Factors 13(6):561–567
51. Jacobs RS, Roscoe SN (1975) Simulator cockpit motion and
the transfer of initial flight training. Proc Hum Factors Ergon
Soc Annu Meet 19(2):218–226
52. Rantanen EM, Talleur DA (2005) Incremental transfer and
cost effectiveness of ground based flight trainers in university
aviation programs. Proc Hum Factors Ergon Soc Annu Meet
49(7):764–768
53. Gray TH, Fuller RR (1977) Effects of simulator training and
platform motion on air-to-surface weapons delivery training.
Air Force Human Resources Lab Brooks Afb Tx
54. Johnson A, Proctor RW (2016) Skill acquisition and training:
achieving expertise in simple and complex tasks. Routledge,
New York
55. Martin EA (1985) The influence of tactual seat-motion cues on
training and performance in a roll-axis compensatory tracking
task setting. Dissertation, Ohio State University.
56. Grundy JG, Nazar S, O’Malley S, Mohrenshildt MV, Shedden
JM (2016) The effectiveness of simulator motion in the transfer
of performance on a tracking task is influenced by vision and
motion disturbance cues. Hum Factors 58(4):546–559
57. Bürki-Cohen J, Sparko AL, Bellman M (2011) Flight simulator
motion literature pertinent to airline-pilot recurrent training
and evaluation. In: Proceedings of AIAA modeling and simula-
tion technologies conference, p 6320
58. Showalter TW, Parris BL (1980) The effects of motion and
g-seat cues on pilot simulator performance of three piloting
tasks. Technical Paper 1601, NASA
59. Casas S, Coma I, Portalés C, Fernández M (2016) Towards a
simulation-based tuning of motion cueing algorithms. Simul
Model Pract Theory 67:137–154
60. Jones ER, Hennessy RT, Deutsch S (1985) Human factors
aspects of simulation: Report of the Working Group on Simu-
lation. Technical Report AD-A159 956, National research
council Washington
61. Meyer GF, Wong LT, Timson E, Perfect P, White MD (2012)
Objective fidelity evaluation in multisensory virtual environ-
ments: auditory cue fidelity in flight simulation. PLoS ONE
7(9):e44381
62. Mulder M, Pool DM, Abbink DA etal (2017) Manual control
cybernetics: State-of-the-art and current trends. IEEE T Hum-
Mach Syst 48(5):468–485
63. Hodge SJ, Perfect P, Padfield GD, White MD (2015) Optimis-
ing the yaw motion cues available from a short stroke hexapod
motion platform. Aeronaut J 119(1211):1–21
64. Reid LD, Nahon MA (1988) Response of airline pilots to
variations in flight simulator motion algorithms. J Aircr
25(7):639–646
65. Väljamäe A, Larsson P, Väställ D, Kleiner M (2008) Sound
representing self-motion in virtual environments enhances lin-
ear vection. Presence Teleop Virt 17(1):43–56
66. Berger DR, Schulte-Pelkum J, Bülthoff HH (2010) Simulating
believable forward accelerations on a Stewart motion platform.
ACM Trans Appl Percept 7(1):1–27
67. Groen EL, Clari MSV, Hosman R (2001) Evaluation of
perceived motion during a simulated takeoff run. J Aircr
38(4):600–606
68. Hosman R, Stassen H (1998) Pilot’s perception and control of
aircraft motions. IFAC ProcVol 31(26):311–316
69. Wentink M, Bos J, Groen EL, Hosman R (2006) Development of
the motion perception toolbox. In: Proceedings of AIAA mod-
eling & simulation conference, p 6631
70. Asadi H, Mohamed S, Lim CP, Nahavandi S (2016) A review on
otolith models in human perception. Behav Brain Res 309:67–76
71. Pais ARV, van Paassen MM, Mulder M, Wentick M (2010)
Perception coherence zones in flight simulation. J Aircr
47(6):2039–2048
72. Groen E, Wentink M, Valente Pais A etal (2006) Motion percep-
tion thresholds in flight simulation. In: Proceedings of AIAA
modeling and simulation technologies conference and exhibit, p
6254
73. Berger D, Bülthoff HH (2009) The role of attention on the inte-
gration of visual and inertial cues. Exp Brain Res 198:287–300
74. Chelette TL, Spenny CH (2003) G & alpha: centrifuge occupant
tolerance to simultaneous high g and high angular acceleration.
In: Proceedings of RTO meeting 86, pp 36.1–36.5
75. Henstenburg RB, Yilmaz C, Richter BH (2004) Design and
analysis of a modern human centrifuge. Sound Vib 4:3
76. Kvrgic VM, Vidakovic JZ, Lutovac MM, Ferenc GZ, Cvijanovic
VB (2014) A control algorithm for a centrifuge motion simulator.
Robot Cim Int Manuf 30(4):399–412
77. Aponso BL, Beard SD, Schroeder JA (2009) The NASA Ames
vertical motion simulator—a facility engineered for realism.In:
Proceedings of royal aeronautical society flight simulation
conference
78. Wentink M, Bles W, Hosman R, Mayrhofer M (2005) Design
and evaluation of spherical washout algorithm for Desdemona
simulator. In: Proceedings of AIAA modeling and simulation
technologies conference and exhibit, p 6501
79. Glaser S, Comtois P (2011) G-pointing: Articulated centrifuge
for real-time G flight simulation. In: Proc. of AIAA Modeling
and Simulation Technologies Conference, p. 6496
80. Casas S, Olanda R, Dey N (2017) Motion cueing algorithms: a
review: algorithms, evaluation and tuning. Int J Virtual Augment
Real 1(1):90–106
81. Valinski R (2020) Examining transient motion cueing and sus-
tained g motion. http://www.etcai rcrew train ing.com/wp-conte nt/
media /produ ct/pdf/Trans ient-Motio n-Cuein g-versu s-Susta ined-
G-Motio n.pdf. Accessed 10 Mar 2020
82. Groen E, Ledegang W, Field J etal (2012) SUPRA-enhanced
upset recovery simulation. In: Proceedings of AIAA modeling
and simulation technologies conference, p 4630
83. Gingras DR, Ralston JN, Oltman R etal (2014) Flight simula-
tor augmentation for stall and upset training. In: Proceedings of
AIAA modeling and simulation technologies conference, p 1003
84. Abramov NB, Goman MG, Khrabrov AN, Soemarwoto BI (2019)
Aerodynamic modeling for poststall flight simulation of a trans-
port airplane. J Aircr 56(4):1427–1440
85. Popovici A, Zaal P, Pieters MA (2018) Time-varying manual
control identification in a stall recovery task under different simu-
lator motion conditions. In: Proceedings of AIAA modeling and
simulation technologies conference, p 2936
86. Nahon M, Reid LD (1990) Simulator motion drive algorithms—a
designer’s perspective. J Guid Control Dyn 13(2):702–709
87. Telban RJ, Cardullo FM (2005) Motion cueing algorithm devel-
opment: human-centered linear and nonlinear approaches. Tech-
nical Report. CR-2005-213747, NASA
88. Pool DM (2012) Objective evaluation of flight simulator motion
cueing fidelity through a cybernetic approach. Dissertation, Delft
University of Technology
89. Reid LD, Nahon MA (1985) Flight simulation motion-base drive
algorithms: part 1—developing and testing the equations. Tech-
nical Report 296, Institute for Aerospace Studies, University of
Toronto

International Journal of Aeronautical and Space Sciences
1 3
90. Reid LD, Nahon MA (1986) Flight simulation motion-base drive
algorithms: part 2—selecting the system parameters. 1986, Tech-
nical Report 307, Institute for Aerospace Studies, University of
Toronto
91. Hosman RJAW, Advani S (2016) Design and evaluation of
the objective motion cueing test and criterion. Aeronaut J
120(1227):873–891
92. International Civil Aviation Organization (2015) Manual of cri-
teria for the qualification of flight simulation training devices,
vol 1—airplanes. Doc 9625, ICAO
93. van Duivenbode E, van Oene E, van Hoof J, Jacobs L, Belleter
D (2019) Objective motion cueing test. Development, strengths,
weaknesses and possibilities. In: Proceedings of AIAA Scitech
Forum, p 0177
94. Vlačić SI, Knežević AZ, Cmiljanović BM (2017) Model of mili-
tary pilot education and training from the aspect of new aircraft
acquisition. Vojno delo 69(4):36–48
95. Toubman A (2020) Calculated Moves: Generating Air Combat
Behaviour. Dissertation, University of Leiden
96. Borgvall J, Castor M, Nählinder S, Oskarsson PA, Svensson E
(2007) Transfer of training in military aviation. Swedish defence
research agency, Command and control systems, (FOI)
97. Bell HH, Waag WL (1998) Evaluating the effectiveness of flight
simulators for training combat skills: a review. Int J Aviat Psy-
chol 8(3):223–242
98. Schreiber BT, Schroeder M, Bennett JW (2011) Distributed
mission operations within-simulator training effectiveness. Int
J Aviat Psychol 21(3):254–268
99. NPA_20_08_Sentenced_Comments on Regulatory Article (RA)
2375: qualification, approval and use of flight simulator training
devices. http://www.thego vernm entsa ys.com/comme nts/16791
15. Accessed 5th Jan 2021
100. Spenny C, Liebst B, Chelette T, Folescu C, Sigda J (2000).
Development of a sustainable-g dynamic flight simulator. In:
Proceedings of AIAA modeling and simulation technologies
conference, p 4075
101. Gawron VJ (2004) The effects of high G environment on humans.
Int J Appl Aviat Stud 4(1):73–90
102. Vidaković J, Kvrgić V, Lazarević M, Dimić Z (2019) Develop-
ment of the algorithms for smoothing of trajectories of a roll and
a pitch axis of a centrifuge motion simulator. In: Proceedings of
7th international SSM congress, pp 1–10
103. Vidakovic J, Lazarevic M, Kvrgic V, Dancuo Z, Lutovac
M (2013) Comparison of numerical simulation models for
open loop flight simulations in the human centrifuge. PAMM
13(1):485–486
104. Crosbie RJ, Kiefer DA (1985) Controlling the human centrifuge
as a force and motion platform for the dynamic flight simulator.
In: Proceedings of flight simulation technologies conference, p
1742
105. Ashworth BR, McKissick BT, Parrish RV (1984). Effects of
motion base and g-seat cueing of simulator pilot performance.
Technical Paper 2247, NASA
106. Ng C, Sondermeyer V, Lewis M (2001) G-cueing system tuning
optimization. In: Proceedings of AIAA modeling and simulation
technologies conference, p 4306
107. Mkhoyan T, Wentink M, van Paassen MM, Mulder M, de Graaf
B (2019) Mitigating the coriolis effect in human centrifuges by
coherent G-misalignment. In: Proceedings of AIAA Scitech 2019
Forum, p 0714
108. Environmental Tectonics Corporation. Tactical flight solutions.
http://www.etcai rcrew train ing.com/tacti cal-fligh t-solut ions/.
Accessed 10 Mar 2020
109. Newman DG (2007) An overview of spatial disorientation as a
factor in aviation accidents and incidents. Report B2007/0063.
Australian Transport Safety Bureau
110. Pennings HJ, Oprins EA, Wittenberg H, Houben MM, Groen
EL (2020) Spatial disorientation survey among military pilots.
Aerosp Med Hum Perf 91(1):4–10
111. Vidaković J, Kvrgić V, Lazarević M, Stepanić P (2020) Com-
puted torque control for a spatial disorientation trainer. FUSME.
https ://doi.org/10.22190 /FUME1 90919 003V
112. Crosbie RJ (1995) The history of the dynamic flight simulator.
https ://pdfs.seman ticsc holar .org/e296/8a969 68901 8be36 b403b
503e3 1f8bb 11860 4.pdf. Accessed 11th Mar 2020
113. Gillingham KK (1992) The spatial disorientation problem in the
United States Air Force. J Vestibul Res 2(4):297–306
114. Pohlmann LD, Reed JC (1978) Air-to-air combat skills: contri-
bution of platform motion to initial training. Technical Report
TR-78-53, AFB, Human Resources Lab
115. Martin EL, Waag WL (1978) Contributions of platform motion to
simulator training effectiveness: study 1—basic contact. Techni-
cal Report TR-78-15, AFB, Human Resources Lab
116. Woodruff RR, Smith JF, Fuller JR, Weyer DC (1976) Full mis-
sion simulation in undergraduate pilot training: an exploratory
study. Technical Report No. AFHRL-TR-76-84, Air Force
Human Resources Lab, Williams AFB
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.