Flight Simulator Experiments Research Articles

Parameter optimization of thermal network model for aerial cameras utilizing Monte-Carlo and genetic algorithm

It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value.

Research on identification of flight cadets’ cognitive load based on multi-source physiological data and CGAN-DBN model

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets’ cognitive load. The research results show that the flight cadets’ cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.

Design and verification of controller for a small coaxial dual-rotor aircraft

In recent years, coaxial dual-rotor aircraft has attracted much attention due to its unique aerodynamic layout. However, it is difficult to miniaturize the coaxial dual-rotor aircraft that relies on the principle of periodic pitch variation due to complex structure and control difficulties. This study proposes a small cylindrical coaxial dual-rotor aircraft based on a vector motor seat. For the control problems of such coaxial dual-rotor aircraft, a position and attitude controller based on neural networks and adaptive cascaded PID control is designed. A control system model was established and tested using MATLAB/Simulink. The proposed control scheme’s soundness and efficiency were confirmed by creating a prototype and performing flight trials. The results show that compared with the classic PID controller, the control effect of the controller has improved by 36.3% to 54.6%. In the centripetal rotation flight simulation experiment, the displacement error does not exceed 0.2 m, the speed error does not exceed 0.05 m/s, and the attitude angle error does not exceed 0.01 rad. This validates the effectiveness of the designed controller, indicating that the controller can enhance the stability of the system and achieve stable hovering and flight.

Theoretical and experimental study on the dynamic behavior of spur gear transmission system during hovering maneuver flights

Gear transmission systems, a crucial component of aircraft power transmission, are subjected to time-varying additional inertial forces and gyroscopic moments in noninertial frame environments when the aircraft performs maneuvering flight actions. In this study, a noninertial frame dynamic model of the gear transmission system during hovering maneuver flights was established, and the accuracy of the theoretical model was verified through conducting hovering maneuver flight simulation experiments. The dynamic behavior of the gear transmission system housing, bearings, and shafts under different hovering maneuver flight parameters was studied. The findings reveal that the maneuvering acceleration of the carrier causes varying degrees of change in the vibration acceleration of the housing, the center distance of the gear pair, the bearing force, and the bending deformation of the shafts, among which the magnitude and direction of the radial bearing force and shaft bending deformation are significantly affected by the maneuvering acceleration. Moreover, the risk of failure can be mitigated through the adjustment of the inclination angle to minimize the bending deformation of the fragile shaft resulting from maneuvering acceleration.

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

PurposeQuadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.Design/methodology/approachFirst, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.FindingsThe figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.Originality/valueThis paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

Angle-of-Attack Estimation for General Aviation Aircraft

The angle of attack is one of the most important flight parameters. In the framework of the present study, a flight data recording method was designed to analyze the Valasek angle-of-attack estimation method and investigate its applicability for general aviation aircraft. This was performed using two devices characterized by substantially different characteristics. The test flight, and the ground test, i.e., a flight simulator experiment, were conducted. Two flight regimes were analyzed: (a) steady climb and descent with low values of angle of attack, (b) approach to stall with idle power with an increase of the angle of attack to the critical value. A satisfactory angle of attack estimate was obtained for the steady climb and descent regime, while the approach to stall estimate was less accurate but still indicative and considered useful for the pilot. The results indicate that less expensive synthetic sensors may provide acceptable results compared to high-quality certified equipment. A proposed modification of the estimation method enables simplification of the required equipment, while offering important information to the pilot.

Modeling eye movement in dynamic interactive tasks for maximizing situation awareness based on Markov decision process

For complex dynamic interactive tasks (such as aviating), operators need to continuously extract information from areas of interest (AOIs) through eye movement to maintain high level of situation awareness (SA), as failures of SA may cause task performance degradation, even system accident. Most of the current eye movement models focus on either static tasks (such as image viewing) or simple dynamic tasks (such as video watching), without considering SA. In this study, an eye movement model with the goal of maximizing SA is proposed based on Markov decision process (MDP), which is designed to describe the dynamic eye movement of experienced operators in dynamic interactive tasks. Two top-down factors, expectancy and value, are introduced into this model to represent the update probability and the importance of information in AOIs, respectively. In particular, the model regards sequence of eye fixations to different AOIs as sequential decisions to maximize the SA-related reward (value) in the context of uncertain information update (expectancy). Further, this model was validated with a flight simulation experiment. Results show that the predicted probabilities of fixation on and shift between AOIs are highly correlated (R = 0.928 and R = 0.951, respectively) with those of the experiment data.

Galvanic vestibular stimulation to counteract leans illusion: comparing step and ramped waveforms

Leans is a common type of Spatial Disorientation (SD) illusion that causes pilots to be confused about the position of the aircraft during a flight. This illusion could lead to serious adverse effects and even flight mishaps. Therefore, an effective means to deal with leans is crucial for flight safety. This study aims to investigate the effects of Galvanic Vestibular Stimulation (GVS) technology with different waveforms as a tool to mitigate the negative effects of leans. 20 Air Force pilots participated in leans-induced flight simulation experiment with three GVS conditions (without-GVS, step-GVS, ramped-GVS). Bank angle error, subjective SD, perceived strength, and annoyance were measured as the dependent variables. Analysis revealed that step-GVS and ramped-GVS yielded lower bank angle errors and subjective SD than without-GVS. In addition, annoyance ratings were lower for ramped-GVS than step-GVS. This study suggests that GVS has the potential to be utilised as a counteracting tool to cope with leans. Practitioner summary: Galvanic Vestibular Stimulation (GVS) can be utilised as a tool to counteract the detrimental effects of leans illusion, specifically the ramped style GVS, considering that it is less annoying and distracting for the pilots. In general, GVS induces a roll sensation that can offset the false sensation caused by the leans, which can potentially help maintain flight safety and avoid spatial disorientation-related accidents. Abbreviations: SD: spatial disorientation; GVS: galvanic vestibular stimulation; MSSQ: motion sickness susceptibility questionniare; SSQ: simulator sickness questionnaire; BLE: bluetooth low energy; PCB: printed circuit board; RPM: revolution per minute

Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot’s physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot’s intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot’s micro-behavior evaluation system.

Pilots’ mental workload variation when taking a risk in a flight scenario: a study based on flight simulator experiments

Pilots’ operation behavior in flight is associated with their mental state variables such as workload, situation awareness, stress, etc. The objective of this study was to investigate the dynamic process of mental workload for pilots who perform a risky flight task in simulated scenarios. Two empirical experiments were conducted to address this issue. In experiment one, 19 trainee pilots divided into high-risk and low-risk groups performed a target-search task in a low-altitude visual flight. The results showed a statistically significant interaction between groups and segments for heart rate variability (HRV). The same pattern of physiological results was replicated among participants in experiment two, in which 19 airline pilots completed an approach with low visibility. These findings highlighted the relationship between mental workload variation and risk-taking behavior, which could be considered in improving pilot selection and training to improve flight safety.

Pilot errors: Communication comes last

This study builds on previous research, which established that in flight simulator experiments the communication performance of pilots was impaired under certain applied conditions. The flight simulator data recording relating to the actions of the pilots were examined to determine the impact of the factors affecting pilots’ communication (increased workload, increased demand on memory and, for some groups, increased ATC speech rate) on their flying performance. Using heading error as the dependent variable, no significant effects were found even for flights where pilots committed the most communication errors. Pilots are taught to prioritise tasks in order of operational safety importance, as per the adage “aviate, navigate, communicate”. Thus, these results are encouraging as they show that the order of operational importance is adhered to, and that flying performance is maintained even when communication is affected.

The Influence of Fatigue on an Instrument Approach

Proven by accident statistics, a tired pilot poses a threat to safe flight operations, especially in its critical phases, such as the approach to landing. In the context of the fatigue's impact on the accurate execution of the approach to landing, no research has yet been carried out to experimentally confirm/deny the impact of this factor on the pilot's performance. Therefore, this study examines the impact of fatigue on the final approach phase through a 24-hour flight simulator experiment. Eight subjects participated in the study, performing 15 instrument approaches over the time period defined above. In order to quantify the stabilised portion of the approach, a time-based approach was defined. The approach was based on identifying the portion of the approach executed in the prescribed descent region of pyramidal shape expressed in percent. The results show a link between the precision and accuracy of the instrument approach execution and the level of pilot fatigue, which can lead to unstabilised approaches that negatively affect overall safety. Therefore, the experimental setup, methodology, and results can serve as an evidence-based approach for a more concrete definition of fatigue risk management or as a knowledge base for further development of this issue.

Adaptive neural tracking control for high angle of attack maneuver with average dwell time

SummaryThis article attempts to study the high angle of attack maneuver from the perspective of switched system control. In view of the complex aerodynamic characteristics, an improved longitudinal attitude motion model is presented, which is a switched stochastic nonstrict feedback nonlinear system with distributed delays. The significant design difficulty is the completely unknown diffusion and drift terms and distributed delays with all state variables. Based on a technical lemma and neural networks, an improved smooth state feedback control law for nonstrict feedback systems is proposed without any growth assumptions. To eliminate the influence of distributed delays, an improved Lyapunov–Krasovskii function is constructed, which skillfully removes the constraint of the upper bound of the delay change rate. Then, by combining the average dwell‐time scheme and stochastic backstepping technique, an adaptive neural network tracking control law is designed, which extends a newly proposed switched system stability condition to the stochastic switched system. Theoretical analysis and flight control simulation experiments are provided to illustrate the effectiveness of the proposed control method.

Against the leans: Overcoming spatial disorientation through galvanic vestibular stimulation

Leans illusion is a type of Spatial Disorientation (SD) that pilots often experience which can adversely affect flight performance. For pilots’ flight safety, research on how to effectively overcome SD such as leans illusion is important. The purpose of this study is to identify the overcoming effect of Galvanic Vestibular Stimulation (GVS) technology on leans illusion. Twenty-one Air Force pilots participated in a flight simulation experiment where leans illusion was induced through a specialized SD simulator. In the with-GVS condition, GVS was given during the roll-out phase. Data was analyzed using roll angle error and subjective SD scales by two conditions (with-GVS, without-GVS). Results showed that both the roll angle error and the subjective SD scale scores were found to be lower in the with-GVS condition than in the without-GVS condition. This study suggests that the use of GVS technology can potentially contribute in overcoming leans illusion.

Go-Around Criteria Refinement for Transport Category Aircraft

Presently, airline pilots are trained to go around if, when lower than 500 ft above the ground, they are outside of a handful of parameters such as airspeed, position, and rate of descent. At times, pilots do not comply with these criteria, perhaps owing to their conservative nature or complexity. This paper examines potential refinements to the continue-to-land decision from the combined results of three flight simulator experiments. Potential refinements include simplifying the number of parameters and lowering the altitude at which pilots make the decision. First, refinements were developed by evaluating pilots’ touchdown performance and qualitative data in a variety of starting and environmental conditions. Second, 30 of those pilots evaluated the refinements under several induced instabilities during the approach. The results showed little difference in touchdown performance when lowering the decision altitude from 500 to 300 ft; however, significant differences arose when the decision altitude was lowered further to 100 ft. The proposed new criteria include assessments of deviations in airspeed and position, no rate-of-descent audio warning, and having an appropriate engine setting at 1000, 500, and 300 ft height above threshold. Additionally, a recommendation is made that if the proposed criteria are not met at the 1000 or 500 ft height above threshold the pilots may make corrections and continue the approach; however, if the criteria are not met at 300 ft, then a go-around should be performed.

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.

Mitigation of Biodynamic Feedthrough for Touchscreens on the Flight Deck

ABSTRACT Biodynamic feedthrough (BDFT) is a key issue for touchscreen operations on the future flight deck, as cockpit accelerations due to turbulence leave pilots vulnerable to erroneous touches that disrupt task performance. This research focuses on the implementation of a software-based cancellation approach to mitigate the adverse effects of BDFT in touchscreen dragging tasks. A flight-simulator experiment with 18 participants was performed to estimate models of BDFT dynamics for horizontal and vertical touch-inputs on a primary flight display. The averaged BDFT models were used to cancel BDFT in the same continuous dragging task used for model identification and a discrete point-to-point dragging task. While for the continuous task the cancellation enabled 63% mitigation in BDFT, the same cancellation was ineffective for the discrete task, due to reduced BDFT susceptibility. Overall, the results show that while model-based BDFT cancellation can be highly effective, a key technical challenge will be ensuring it is sufficiently task-adaptive.

Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments

Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot’s cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety.

Errors in air-ground pilot communication: an experimental study

Understanding aviation communication has obvious implications for flight safety and for the training of pilots and Air Traffic Controllers (ATCs). Its study also sheds light on the use of Aviation English as a type of English for a very specific purpose and on real-world communication constraints in a high-stakes environment. We present results from a study combining human factors and linguistics, in which pilots were recorded in flight simulator experiments probing communication accuracy under diverse conditions. An analysis of the audio recordings in terms of whether errors were omissions or mistakes, whether errors occurred with words or numerals, and which types of aviation information were affected, revealed complex interactions between language background, pilot qualification, workload and ATC speech rate. The differences between native English speakers (NES) and non-native English speakers (NNES) are complex and more nuanced than might be expected. These results not only are relevant for aviation safety, training and testing, but they also provide insights on communication under challenging conditions and on the relation between language proficiency and cognition, with implications for second language teaching and the use of English for Specific Purposes (ESP) in highly constrained environments.

Migration behaviour of commercial monarchs reared outdoors and wild-derived monarchs reared indoors.

Captive rearing of monarch butterflies is a commercial and personal pursuit enjoyed by many different groups and individuals. However, the practice remains controversial, especially after new evidence showed that both a group of commercially derived monarchs reared outdoors and a group of wild-derived but indoor-reared monarchs failed to orient south, unlike wild-derived monarchs reared outdoors. To more fully characterize the mechanisms responsible for the loss of orientation in both commercial and indoor-reared monarchs, we performed flight simulator experiments to determine (i) whether any fraction of commercial monarchs maintains a southern heading over multiple tests, and (ii) whether indoor conditions with the addition of sunlight can induce southern flight in wild-derived monarchs. Commercial monarchs changed their flight direction more often over the course of multiple tests than wild-derived monarchs. While as a group the commercial monarchs did not fly south on average, a subset of individuals did orient south over multiple tests, potentially explaining the discordance between flight simulator assays and the recovery of tagged commercial monarchs at overwintering locations. We also show that even when raised indoors with sunlight, wild-derived monarchs did not consistently orient south in the flight simulator, though wild-derived monarchs reared outdoors did orient south.