Simulation: flight simulation

A method for developing and performing a rotorcraft flight simulation in a bluff body airwake is investigated and compared to flight test data. This method is broadly composed of two parts: an atmospheric boundary layer computational solution that is unsteady and time-accurate, and a UH-60 flight dynamics program that can be coupled with an airwake developed from the atmospheric boundary layer solution. The flight dynamics program is a MATLAB/SIMULINK implementation of the GENHEL model. The computational fluid dynamics airwake solution is developed to recreate the airwake present for the validation case flight tests that were used to collect the flight test data. The validation case f light test data was collected from a variety of station-keeping tasks performed in the wake of a hangar on land. Two kinds of flight simulations were performed: piloted simulations in a flight simulator facility and flight simulations performed using a pilot model. Comparisons of the power spectral density analyses of pilot control stick activity reveal that pilots in the simulated flight tests use a generally higher level of control activity energy than the pilots of the f light test validation case. Limited simulator flight test data showed sizeable differences in pilot control activity for the same simulated flight task flown by different pilots. Significant unknowns remain in comparing a simulated flight to a physical flight. The pilot model was not used to compare simulated flights to validation case flights, but was useful for comparing different flight simulation cases to each other as well as for analyzing sensitivity of flight simulations to positioning error within a simulated airwake. The pilot model flight simulations show that flight tasks performed in an airwake produced using a high-fidelity atmospheric boundary layer simulation require more pilot control input power than airwakes made using a comparable mean wind speed uniform inflow.

The use of virtual reality (VR) for flight simulation, particularly in the earliest stages of pilot training, is gaining attention in both research and industry. The use of the technology for this ab initio training requires suitable consideration of the risks of simulator sickness—risks that are heightened relative to conventional simulators. If simulator sickness results in the development of compensatory skills, or otherwise disrupts the training process, the benefits of the technology may be negated. Enabling the effective integration of VR within flight training requires that, to the extent that simulator sickness is an issue, practical mechanisms are developed to manage the occurrence without disrupting existing training structures. The primary objective of this research is, thus, to evaluate an intervention and a nuisance factor in relation to the reduction of simulator sickness, considering their practicality within existing flight training syllabi. The Total Severity (TS) of the Simulator Sickness Questionnaire (SSQ) was evaluated within a quasi-experimental, non-equivalent pre-test–post-test design, incorporating three groups: a prior flight experience nuisance factor group, a prior personal computer aviation training device (PCATD) exposure intervention group, and a control group with neither prior experience nor prior simulator exposure. The results indicated that the TS was significantly reduced for the prior flight experience nuisance factor (rrb = 0.375), but that the PCATD exposure intervention produced no such reduction (rrb = 0.016). The findings suggest that VR flight simulation is likely best used as a supplemental tool, introduced after initial airborne experience. Notwithstanding this finding, the relatively low median TS scores (<20) for all groups suggest that the technology may still be used with caution earlier in the training process. No other published research has examined this important effect in the context of the new VR situation.

This book presents the steps necessary for the application of rotorcraft flight modelling and simulation and analysis in support of certification for compliance with EASA CS-27 and CS-29, SUBPART B—FLIGHT and other flight-related rules (e.g. CS-27/9, Appendix B, Airworthiness Criteria for Helicopter Instrument Flight). The material is presented in the form of a structured ‘Rotorcraft Certification by Simulation’ process, starting from the relevant paragraphs in the Certification Specifications, through a comprehensive description of the assembly of flight simulation requirements, informed by judgements on Influence, Predictability and Credibility, and on into the detailed building of the three major elements of the process; the Flight Simulation Model, the Flight Simulator, and the associated Flight Test Measurement System.

Object detection on the basis of relative motion was investigated in the fly at the neuronal level. A representative of the figure detection cells (FD-cells), the FD1b-cell, was characterized with respect to its responses to optic flow which simulated the presence of an object during translatory flight. The figure detection cells reside in the fly's third visual neuropil and are believed to play a central role in mediating object-directed turning behaviour. The dynamical response properties as well as the mean response amplitudes of the FD1b-cell depend on the temporal frequency of object motion and on the presence or absence of background motion. The responses of the FD1b-cell to object motion during simulated translatory flight were compared to behavioural responses of the fly as obtained with identical stimuli in a previous study. The behavioural responses could only partly be explained on the basis of the FD1b-cell's responses. Further processing between the third visual neuropil and the final motor output has to be assumed which involves (1) facilitation of the object-induced responses during translatory background motion at moderate temporal frequencies, and (2) inhibition of the object-induced turning responses during translatory background motion at high temporal frequencies.

Loss of control in-flight (LOC-I) has historically been the leading cause of commercial airline fatalities. In 2009, an investigation of LOC-I accidents revealed four primary causal factors, citing human factors errors as one of the factors. The investigation discovered that one reason for the human errors was due to deficiencies in pilot training from aircraft stalls and upsets. These deficiencies led the FAA and similar international organizations to implement upset prevention and recovery training (UPRT) guidelines for commercial air transport aircraft. The aviation industry uses flight simulators as part of a comprehensive program for pilot training and certification requirements. Although flight simulators can possess a high level of realism, deficiencies remain. Many pilots find simulators to be easier than their real-world counterparts. Even with a high-fidelity FFS, simulator training currently cannot accurately reproduce essential factors that are considered to be within the safety margins of the aircraft, such as roll and pitch rates and g-loading, all of which can lead to high levels of altitude loss, if not managed correctly. This thesis compared upset recovery maneuvers between a simulator and live flight. The study used in the research required the pilots to recover from an upset condition while also performing an additional task of serially adding two numbers. The secondary task provided a physiological baseline cognitive workload measurement that was used to compare the pilots’ increased workload levels during the recovery phase of the flight. The results indicated that the pilots generally performed better during the simulator flights and had less subjective workload and high levels of situation awareness. The physiological-based workload contrasted subjective measures. However, during a live flight upset recovery, pilots may not be able to transfer the skills acquired from a flight simulator and experience higher levels of workload and reduced levels of situation awareness, increasing their potential for an unsuccessful recovery. Suggestions are made that include more use of live aircraft aerobatic training for UPRT and the use of a physiological-based, unbiased workload assessment during pilot training. Additionally, it is suggested using an adaptive training approach to UPRT that incorporates the primary technical flight performance metrics with secondary task performance.

: The purpose of this study was to assist in the development of flight duration parameters for scheduling aircrew work-rest cycles. Twenty-four Airmen were selected to represent the USAF pilot population on the basis of flight aptitude scores, class II flight physicals, and personal interviews. Each received an intensive 7-day flight program in Link GAT-1 Trainers. Then half of the subjects were randomly assigned to a schedule that regularly alternated 12- hr duty days with 12-hr rest periods for 4 days. The remaining subjects followed a scheduled with duty days of 12, 24, 24, and 12 hr, with a 12-hr rest period between duty days. During each duty day, all subjects flew two 4.5-hr simulated flights. Flying performance was evaluated every hour by both simple and complex flight maneuver tests, administered and scored by a PDP-12 computer. The subject's threshold of information processing speed was determined hourly by a Discrete Information Processing Test (DIPT). (The DIPT is an adaptive, computer- controlled task developed for eventual use in the actual flight environment.) Continuous heart rate (HR), heart rate variability (HRV), fatigue, sleepiness scores, and sleep logs were collected throughout each mission.

Automation's effects on workload and vigilance were examined in a flight simulator. Nonpilots performed a single-axis tracking task, using the directional gyroscopic compass (gyro) as a cue to fly a constant heading during turbulence. They also monitored for two instrument abnormalities: motion of a normally stationary dial located 10° of visual angle horizontally from the gyro and freezing of the attitude indicator (normally constantly moving due to the turbulence), located 4° vertically from the gyro. Participants flew in both manual and autopilot tracking conditions. The autopilot significantly reduced both workload (indexed by heart rate variability) and response time to the attitude failure. Response times to the navigation dial event, but not to the attitude event, significantly increased with time regardless of automation level. Results show that vigilance declines during simulated flight, and automation can reduce mental workload but does not eliminate the vigilance decrement.

Introduction I August 1988, the Singer Corporation demonstrated a relatively simple networking of the AH-64 combat mission simulator (CMS), AH-1 flight and weapons simulator (FWS), and UH-60 flight simulator (FS) at Fort Rucker. At the end of this demonstration, three night shifts were made available for a quick research evaluation of this capability. One AH-64 and one AH-1 crew volunteered to participate in an air-to-air combat (ATAC) pilot study involving their respective aircraft. The sample size of this pilot study is inadequate to draw any conclusions with certainty. However, in this instance, there is so little information on the use of simulators in training helicopter AT AC that even the tentative suggestions of a pilot study are of interest. All reference to weapons performance has been deleted for security reasons.

This paper details an analysis of different visualization systems for use in an academic flight simulator, Future Systems Simulator (FSS). First, an overview of off-the-shelf flight simulators is done, detailing the primary features of flight simulators such as Flight Gear, Prepar3D, X-Plane, and Microsoft Flight Simulator (2020). Then, the current setup of the FSS is presented (which uses FlightGear), followed by the process of introducing X-Plane as a scenery-generation tool. To conduct a comparative analysis between FlightGear and X-Plane visual systems, a total of twelve participants with varying levels of experience were invited to participate in the study. The participants performed flight trials in a simple landing scenario at Heathrow Airport. Additionally, the more complex approach at London City Airport was performed with a group of only four highly experienced participants. Participants then gave their feedback and completed a questionnaire. The data from their attempts were recorded for qualitative and quantitative comparison. The results were analyzed to determine which of the two visual systems could be used in the FSS moving forward.

Flight simulators can recreate aircraft flights for flight training and aircraft design. This paper reviews existing engineering flight simulators and introduces the development of a multi-purpose reconfigurable engineering flight simulator at Ryerson University. The multi-purpose engineering flight simulator named the Ryerson Fixed Base Simulator (RFBS) has been designed and built to teach and initiate research projects in the area of aircraft design, flight simulation, pilot training, and flight data analysis. It consists of three 46 inch high definition screens and six 22 inch touch screen panels to represent the instrument panel, the centre console, and the overhead panels of an actual aircraft flight deck. Several low-cost, commercial flight simulation software were tested and X-Plane was selected as the main flight simulation tool. This paper also introduces a research that utilizes the RFBS and a commercial flight analysis software. The objective of the research was to develop a flight data conversion methodology. A case study was commenced to verify the work flow of flight data generation and analysis with an example of hard-landing analysis.

Flight simulators have been used for a long time to train pilots of any type of aircraft. This type of training is more economical. The flight simulator improves control skills in extreme situations with minimal risks for the future pilot. Training on a flight simulator makes it possible to reproduce adverse weather conditions at any time. The pilot is on the ground in a special cockpit, which is mounted on a movable platform. This system is needed to ensure flight conditions as close to real as possible. The subject of the research in the article is an aviation simulator on the Stewart platform. The aim is to create and analyze an flight simulator platform using software. The paper presents the already existing flight simulator and its characteristics. Taking into account its dimensions and weight, the platform is modeled in SolidWorks. The kinematic diagram of the type design (6-6) is selected and presented. The basic equation of dynamics for calculating platform motion law for given values of the control forces is given. Also using this equation it is possible to find the controlling forces knowing the law of motion. The developed 3D model consists of two platforms, a movable and a stationary one. The legs with automatically variable length are attached to the platform using hinges. Selected material aluminum alloy type 2024 from which the platform is created. Selected material aluminum alloy type 2024 from which the platform is created. Static analysis of the loaded platform was performed. By loaded platform is meant that in the places where the simulator and the pi-lot's seat are installed, loads equal in weight to the simulator and the maximum weight of the seat with the pilot are applied. The anal-ysis includes such epurfaces: loads to assess whether the structure can support a given weight, displacement, deformation, safety margins and a Design Insight plot to evaluate design details. Identification of elements that are most likely to start collapsing under the weight of the simulator. The research was performed using SolidWorks Simulation software. Based on the data obtained from the plot, conclusions were made about the performance and safety of the developed platform.

Complex livestock production problems are barriers to achieving long-term sustainability and profitability. Mathematical nutrition models are an integral component of daily livestock feeding systems, as they help improve animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Different actors, such as research institutions, single-owner farmers or ranchers, integrated livestock producers, or technology providers, have different needs for mathematical models. Many actors use spreadsheets and existing empirical models for nutrition and management, while others use mechanistic, dynamic, or artificial intelligence models. Regardless of model type, understanding the complexity of livestock systems is important, especially as many modeling projects often fail to give new insights to experienced actors within these systems. Flight simulators, also known as decision support tools, help identify high-leverage solutions while causing learning that leads to insight. However, understanding interrelationships between the parts of a system is difficult because intended and unintended consequences are often delayed. Our article reviews the pipeline of developing a flight simulator and contextualizes it within relevant technological-economic-agriculture issues, such as the growing use of precision livestock farming and associated cyberinfrastructure concerns. Fully documented and easy-to-use flight simulators provide a means to introduce stakeholders (actors) to a methodology for generating insights, as users design and run model scenarios for themselves, thereby challenging their mental models. This experiential learning is often more impactful (compared with someone telling them) because insight is gained as the actors challenge their own mental models through inductively or deductively assessing equations, parameters, and outcomes. As precision livestock technology increases on-farm, so do the intended and unintended consequences of data collection, maintenance, model development, deployment, and scale for enhancing animal production. Flight simulators help actors anticipate desired reinforcing actions such as increasing efficiencies and balancing actions within systems that slow the growth of efficiencies such as new training, labor, or infrastructure to manage large datasets or cyberinfrastructure concerns.

This paper describes the use of Microsoft's “Flight Simulator” and “Flight Simulator: Aircraft and Scenery Designer” in teaching human factors in aviation to psychology and engineering students. The student's role commenced as that of a participant-observer and concluded as that of a scientist-practitioner. Flight Simulator was used to familiarize the student with aerodynamic principles, the relationship between aircraft controls and displays and flight dynamics. Both the syllabus incorporating the flight simulation and the capabilities of the simulation are described. The authors examined the capabilities of the simulations and feel that they provide an excellent return for a comparatively small investment. Students felt that the ability to use a simulator greatly enhanced the course. The simulations have potential applications which remain to be tapped. As a teaching aid, it is rated “best buy”.

Advances in flight simulator performance fidelity, structural and mechanical design techniques and aerospace physiology research have enabled the development of a new generation of flight simulators. Through the integration of a high performance planetary motion centrifuge with a gimbaled, flyable, cockpit module, this new class of motion system is capable of replicating G forces experienced in flight. All axes accelerations are utilized to actuate the gimbal system that can be rotated in pitch and roll to align the cockpit with the centrifugal and tangential acceleration generated by the centrifuge. The effect of this softwarehardware system is to constantly align the G-vector at the pilot’s seat to the direction in flight. These devices have come to be known as Continuous G Devices (CGDs). This paper discusses the mechanical hardware behind CGDs in addition to physiological factors involved in both hardware and software design. It further explains the integration of flight simulation architecture into the motion of the device. Rudimentary parameters are examined for accuracy in CGD flight simulation. Additionally, practical evaluations of tactical aviation, commercial aircraft upset prevention and recovery to sub-orbital and orbital space flight are presented. Comparisons are made between simulator performance and current regulatory requirements for legacy devices where applicable. Finally a synopsis of research demonstrating the performance of these devices is presented.

Research of Compound Controller for Flight Simulator with Disturbance Observer