Aircraft Cockpit Research Articles

AI Collaboration In Aircraft Cockpit Systems

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Dynamic characteristics and vibration control of composite laminate wall panels in electric aircraft using NiTi shape memory alloys

This paper investigates the dynamic characteristics and vibration control of a new-energy electric aircraft cockpit wall panel. An innovative boundary-splitting Rayleigh-Ritz method is introduced for modal analyses of wall panel structures in thermal environments, verified with the hammering and finite element methods, showing a maximum error of 5.42 %. NiTi shape memory alloy (SMA) wires are embedded in composite structures for vibration control, with the first theoretical-experimental validation performed. A parametric fitting-computational model for NiTi-SMA is developed to extract mechanical parameters from hysteresis data. Theoretical models are decoupled into dynamical equations using the Galerkin truncation method. The damping capability of NiTi-SMA is demonstrated by solving the amplitude using the harmonic balance and Runge-Kutta methods. Room-temperature and variable-temperature vibration tests show over 30 % vibration control capability with optimized embedding methods. These experimental results confirm the theoretical findings, providing support for the analysis and vibration control of new-energy aircraft.

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.

An information flow field model for human–machine interaction

PurposeAbout 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.Design/methodology/approachThe performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.FindingsThe results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.Originality/valueThe novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

Математическая модель сопутствующей деятельности пилота и ее применение для объективной оценки его состояния и профессиональной подготовки

<p>A new approach to analysis and diagnostics of the pilots’ activities is presented, which makes it possible to compare both different options of the aircraft cockpit information and control field and the professional training of flight personnel from the viewpoint of application effectiveness. A mathematical model of the associated activities in use represents the pilot work via the matrix of transition probabilities, where transitions occur between the operations he performs. The approach under consideration is based on the convolution of the applied Markovian processes, which are created using the pilot activity protocols, into the quantum representations, making it possible to reveal the structure of these processes by the quantum spectral analysis. Application of the quantum filtering makes it possible to obtain quantitative diagnostic assessments of the pilot activity. Analysis of the experimental results for pilots of various training levels is given to demonstrate the possibility for assessing their professional skills as well as the prospects for using the proposed methods.</p>

Experimental study on visual ergonomics of an aircraft cockpit considering an extremely wide range of illuminance conditions

Vision is the main way for pilots to obtain information, and good visual ergonomics are an important support for ensuring aircraft flight safety. The range of illumination changes in the light environment of the aircraft cockpit is very wide, and research on the visual ergonomics of the cockpit needs to consider various extreme lighting conditions. This study conducted visual ergonomics experiments on 15 participants in a full-scale simulated cockpit, examining the accuracy, reaction time, and subjective evaluation of visual tasks under 8 typical environmental lighting intensity levels. The experimental results show that, except for head-up display, the accuracy of visual target interpretation tasks performed by other display devices under different brightness conditions remains at a high level. And as the brightness of the display device increases, the accuracy of interpretation gradually increases, and the reaction time gradually decreases. In terms of subjective evaluation, there is a significant correlation between fuzziness, fatigue, clarity of image symbols, resolution between symbols, comfort of the image, and overall satisfaction with the image, but the correlation with environmental illumination level is relatively low. The experimental results can provide a certain theoretical basis for the design of cockpit lighting environment.

Artificial Intelligence for Real-Time Tolerance to Critical Flight Data Errors in Large Aircraft

The environment in the cockpit of large transport aircraft is highly complex due to an increasing number of automation systems. This complexity can cause pilots to become less aware of how systems interact. It becomes a severe issue when sensor or data failures occur, as such failures can contribute to a situation in which it is difficult for a pilot to assess what actually is happening and, possibly, how to resolve the problem. This paper presents a method, based on artificial intelligence, for identifying incorrect critical flight control data in real-time. A novel combination of reinforcement learning and a denoising autoencoder is proposed to identify failures and to provide inputs to the aircraft’s flight control and guidance systems, allowing for the correct maneuver to counter the failure and/or to avoid or recover from flight upsets. Tests in stall conditions with a partially blocked pitot tube show that the proposed method results in successful detection and recovery. The performance of the system without an autoencoder is compared to highlight the significant advantages, how this relates to creating systems with AI to improve situational awareness for pilots, and to execute appropriate automatic maneuvers to successfully counter the effect of sensor failures.

Effects of Task Proficiency and Automation Reliability on Pilot-ePilot Teaming in Intelligent Aircraft Cockpits

Intelligence of aircraft cockpits can be improved by creating an electronic pilot (ePilot) to establish a Pilot-ePilot teaming relationship. Effects of task proficiency and automation reliability on such kind of human-autonomy teaming were studied, including task performance, task load, situation awareness, trust in automation, and human errors. In a 2 × 3 within-subjects experiment (low, medium & high levels for task proficiency and automation reliability), 21 male participants completed simulated flight tasks within local airport traffic patterns. Participants were teamed up with a system which took over the flight control priority based on task requirements and cognitive states. Experimental data was analyzed by statistical tests on significance and effect sizes. Results indicated that automation reliability influenced subjective and objective measures more than task proficiency, while task proficiency partly compensated the loss of performance due to deteriorated automation reliability. Findings support that the pilot-centered design principle should still be adhered to for Pilot-ePilot teaming in intelligent aircraft cockpits.

Increasing predictability in the response of an AI-assisted stall recovery system in complex stall conditions by expanding the knowledge-base of AI

The environment in the cockpit of commercial aircraft is becoming increasingly complex due to the introduction of automation systems. This complexity is especially evident when malfunctions take place, making it difficult for pilots to comprehend the interconnectedness of the systems and potentially leading to loss of control. This paper investigates a novel method for creating an Artificial Intelligence-based stall recovery assistant using Reinforcement Learning by training the agent to generate a stall and subsequently recover from it. This enables training in a large training space with a simple reward function, where the agent has the ability to develop a deep understanding of the environment. Tests show that the agent is able to recover from stall at a variety of altitudes while experiencing unreliable airspeed information originating from a blocked Pitot tube system and with a better response than all baseline agents. The results indicate that restricting AI is not always necessary and, further, that too many restrictions can lead to a system that learns only shallow features, causing it to be unreliable in unforeseen circumstances.

The research of touch screen usability in civil aircraft cockpit.

With the advancement of touch screen technology, the application of touch screens in civil aircraft cockpits has become increasingly popular. However, further analysis and research are required to fully promote its applications. The paper researched the usability of touch screens in aircraft cockpit considering the operation performance and subjective NASA-TLX workload evaluation, conducted experimental research on three touch gestures: click, drag, and zoom. Additionally, a comparative analysis was conducted on the touch performance under different layouts, positions, touch sizes, dragging direction angles, and zoom multiples. The touch performance indicators include operation time, error rate, operation speed, and workload. The experimental results show that the 21 mm size has the minimum operation time and workload, and 18 mm size has the lowest error rate in the clicking tasks. Additionally, the performance and workload of the captain's layout are better than those of the co-pilot's layout, and the performance of the center console position is best. The operation speed of the dragging tasks is faster when performed at position R3 compared to other positions. The dragging moving angles with better operation speed are 80°-190° and 250°-290°. The operation performance and workload of the zooming tasks vary depending on the zoom multiples. As the multiple increases, the operation time and workload also increase. There is no difference in operation performance or workload between zooming in and zooming out. The paper provides experimental support and suggestions based on human operation and subjective NASA-TLX workload evaluation for the application of touch screens in civil aircraft cockpits.

Aircraft Cockpit Window Improvements Enabled by High-Strength Tempered Glass

<div>This research was initiated with the goal of developing a significantly stronger aircraft transparency design that would reduce transparency failures from bird strikes. The objective of this research is to demonstrate the fact that incorporating high-strength tempered glass into cockpit window constructions for commercial aircraft can produce enhanced safety protection from bird strikes and weight savings. Thermal glass tempering technology was developed that advances the state of the art for high-strength tempered glass, producing 28 to 36% higher tempered strength.</div> <div>As part of this research, glass probability of failure prediction methodology was introduced for determining the performance of transparencies from simulated bird impact loading. Data used in the failure calculation include the total performance strength of highly tempered glass derived from the basic strength of the glass, the temper level, the time duration of the load, and the area under load.</div> <div>A high-strength transparency construction developed using advanced technology tempered glass could produce significant weight saving estimated at 28% for a typical Boeing 737 jetliner window, or about 11.5 kg for the four windows of the Boeing 737 flight deck.</div> <div>The conclusions from the principals developed in this research for an advanced transparency construction including a 0.378-in. thick high-strength core ply could reduce the probability of failure from bird strike loading from 4,503 to 953 parts-per-million (PPM). A 0.500-in. thick high-strength standard thickness core ply could reduce the probability of failure from bird strike loading from 4,503 to 1.7 PPM.</div>

A comprehensive assessment of environmental factors in aircraft cockpit based on fuzzy evaluation.

The cockpit of an aircraft is the main place where the pilot controls the aircraft on a mission. An excellent cockpit environment not only ensures the pilot's basic survival needs but also improves the comfort level and alleviates fatigue when performing missions. On the basis of domestic and international airworthiness standards, a top-down refinement method is deployed to determine the initial goal, and the environmental criteria are fully discussed and balanced in a dynamic process to build a comprehensive evaluation system for environmental factors in the aircraft cockpit. Based on the fuzzy comprehensive evaluation theory, an evaluation model for environmental factors is constructed by combining analytic hierarchical analysis (AHP) and particle swarm optimization (PSO). Then the feasibility of the evaluation model is verified by an illustrative example. The results suggest that the light environment gains the highest score among the 4 environmental criteria followed by the thermal environment, while both sound environment and microenvironment have relatively low scores. As for the 27 environmental sub-criteria, temperature, illumination, lighting clarity, light-color coordination, noise duration and pressure score the highest. The evaluation findings can provide important environmental control criteria for the subsequent environmental control system in the cockpit of the aircraft.

НЕЙРОСЕТЕВАЯ СИСТЕМА ЛАЗЕРНОЙ ДИАГНОСТИКИ ЭЛЕМЕНТОВ ОСТЕКЛЕНИЯ КАБИН САМОЛЕТОВ

Assessing the technical condition of glazing elements in the cockpits of operational-tactical aircraft still remains the most important task in ensuring flight safety. To increase the efficiency of operations for non-destructive testing of glazing elements using the speckle structure method of optical radiation, the authors propose to use neural network technologies to automatically identify controlled areas in the cockpit. Artificial intelligence technologies have been used to realise this task. They are based on algorithms of semantic segmentation, classification and detection of monitored areas according to the established markers on the cabin due to the application of convolutional neural network on YOLOv8. The application of machine vision technology have made it possible real-time measurement of the glazing exit from the termination when overpressure has been created inside the cabin. This reduces the time for technical condition assessment by at least 10 times. The use of machine vision technologies have made it possible to measure the value of the glazing outlet from the sealing in real time when creating excessive pressure inside the cabin and thereby reduce the time to assess the technical condition by at least 10 times. The authors have established the reason for the discrepancy between the results of using the speckle-structure method of optical radiation in determining the value of glazing yield from the termination and the "tape" method and developed recommendations to reduce measurement errors.

Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.

Research on Visual Fatigue Caused by Color Combinations in Civil Aircraft Cockpit Display Interfaces

In order to analyze the visual fatigue degree of the foreground and background color combination of the display of civil aircraft cockpit, the visual afterimage experimental paradigm is used in the present study. Objective eye movement data and subjective reported visual afterimage color were collected. The difference of pupil diameter under different color combinations showed that background color had a significant effect on visual fatigue. In addition, black background color can lead to the lowest degree of visual fatigue, followed by brown background color. Moreover, blue background color was the most likely to cause visual fatigue. The subjective report data showed that the visual afterimage color was related to the foreground and background color combination. In the combination of background color and foreground color, the ratio of visual afterimage color and background color is the highest, followed by the foreground color, and the complementary color of foreground color or background color. Based on the above results, the color design suggestions of civil aircraft cockpit interface are proposed in this work.

Aircraft Cockpit Interaction in Virtual Reality with Visual, Auditive, and Vibrotactile Feedback

Safety-critical interactive spaces for supervision and time-critical control tasks are usually characterized by many small displays and physical controls, typically found in control rooms or automotive, railway, and aviation cockpits. Using Virtual Reality (VR) simulations instead of a physical system can significantly reduce the training costs of these interactive spaces without risking real-world accidents or occupying expensive physical simulators. However, the user's physical interactions and feedback methods must be technologically mediated. Therefore, we conducted a within-subjects study with 24 participants and compared performance, task load, and simulator sickness during training of authentic aircraft cockpit manipulation tasks. The participants were asked to perform these tasks inside a VR flight simulator (VRFS) for three feedback methods (acoustic, haptic, and acoustic+haptic) and inside a physical flight simulator (PFS) of a commercial airplane cockpit. The study revealed a partial equivalence of VRFS and PFS, control-specific differences input elements, irrelevance of rudimentary vibrotactile feedback, slower movements in VR, as well as a preference for PFS.

Prefetch and Push Method of Flight Information Based on Migration Workflow

As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.

Mechanical Device or Touchscreen Widget: The Effects of Input Device and Task Size on Data Entry on the Primary Flight Display

Due to their customizability, touchscreens continue to advance as a device of choice when designing aircraft cockpits. Previous studies investigated the effect of turbulence on human performance when using touchscreens, but few have evaluated its performance for realistic aviation-specific tasks. In this study, we compared four touchscreen widgets and three mechanical devices during realistic data input on a primary flight display (PFD). Twenty participants took part in the experiment at a constant level of vibration, while simultaneously completing a secondary tracking task. Results indicated that virtual keypads lead to faster completion time for medium to large changes while keeping error rates low. Rotary knobs were fastest for small changes. Virtual keypads also had lower workload and discomfort compared to rotary knobs and drag-based widgets. We found the completion time to be the most important factor in tracking task performance, which translated in higher precision for keypads. These findings suggest that virtual keypads represent an efficient and secure option for numerical data input at low-to-medium vibration.

Practical examples for the flight data compatibility check

Accurate information about aircraft speed, altitude, and aerodynamic flow angles is essential for evaluating aircraft performance and handling qualities. These quantities are determined from air data measurements taken by sensors normally located near the aircraft cockpit. Since these sensors are affected by the distorted flow field around the fuselage, a correction must be applied. Before the first flight, a set of calibration parameters is usually determined from wind tunnel experiments or CFD calculations. However, the Data Compatibility Check (DCC) method allows a more accurate air data sensor calibration during the certification flight test. This method reconstructs air data quantities from inertial acceleration, angular rate measurements and the flight path. By comparing the reconstructed quantities with the measured ones, the structure and parameters of air data sensor models can be identified. In this paper, an introduction to the data compatibility check method and the setup used in a flight test for system identification is given. The DCC is applied on data gathered from a test campaign with the new DLR research aircraft Dassault Falcon 2000LX ISTAR. Use cases for the calibration of the nose boom airflow vanes and the correction of sensors during large sideslip maneuvers will be presented in this paper.

Retracted: Study on Aircraft Cockpit Function Based on Neural Network

Retracted: Study on Aircraft Cockpit Function Based on Neural Network