Usage of High-Fidelity Software-Based Avionics Components in Flight Simulation Systems

Author(s):  
Brian Huggins
Author(s):  
Yan Zhang ◽  
Seamus McGovern

Mathematical models are presented in this paper to describe human maneuvers for aircraft flight simulation. Input parameters for the human pilot model (HPM), such as the course deviation indicator (CDI) and the heading change, are defined for the model, and are related mathematically to those in the proportional-integral-derivative (PID) controller for automatic control. Similarities are discussed between the parameters in HPM and those in the automatic control for better understanding of the significance of human factors and their effect on aircraft behavior. Examples for the HPM include aircraft instrument landing system (ILS) lateral and vertical control, heading change, and homing. The model is tested by using the high-fidelity flight simulation simulator JSBSim [1].


Author(s):  
Daeyeon Lee ◽  
Nhu Van Nguyen ◽  
Maxim Tyan ◽  
Hyung-Geun Chun ◽  
Sangho Kim ◽  
...  

Using the global exploration and Kriging-based multi-fidelity analysis methods, this study developed a multi-fidelity aerodynamic database for use in the performance analysis of flight vehicles and for use in flight simulations. Athena vortex lattice, a program based on vortex lattice method, was used as the low-fidelity analysis tool in the multi-fidelity analysis method. The in-house high-fidelity AADL-3D code was based on the Navier–Stokes equations. The AADL-3D code was validated by comparing the data and the analysis results of the Onera M-6 wing and NACA TN 3649. The design of experiment method and the Kriging method were applied to integrate low- and high-fidelity analysis results. General data tendencies were established from the low-fidelity analysis results. The high-fidelity analysis results and the Kriging method were used to generate a surrogate model, from which the low-fidelity analysis results were interpolated. To reduce repeated calculations, three design points were simultaneously added for each calculation. The convergence of three design points was avoided by considering only the peak points as additional design points. The reliability of the final surrogate model was determined by applying the leave-one-out cross-validation method and by obtaining the cross-validation root mean square error. Using the multi-fidelity model developed in this study, a multi-fidelity aerodynamic database was constructed for use in the three degrees of freedom flight simulation of flight vehicles.


2021 ◽  
Vol 13 (3) ◽  
pp. 13-27
Author(s):  
Yamina BOUGHARI ◽  
Ruxandra Mihaela BOTEZ ◽  
Amir BANIAMERIAN ◽  
Ehsan SOBHANI TEHRANI ◽  
Armineh GARABEDIAN

Simulating an aircraft model using of high fidelity models of subsystems for its primary and secondary flight control actuators requires measuring or estimating aero-load data acting on flight control surfaces. One solution would be to incorporate the data recorded from flight tests, which is a time-consuming and costly process. This paper proposes another solution based on the validation of an aero-loads estimator or on the hinge moments predictor for fully electrical aircraft simulator benchmark. This estimator is based on an aerodynamic coefficient calculation methodology, inspired by Roskam’s method that uses the geometrical data of the wing and control surfaces airfoils. The hinge moment values are found from two-dimensional lookup tables where the deflections of the control surfaces, aircraft altitude, and aircraft angles of attack are the input vectors of the tables; and the resulting hinge moment coefficients are the output vectors. The resulting hinge moment coefficients of the Convair 880 primary flight control surfaces are compared to those of its recorded flight test data; the results from the new software solution were found to be very accurate. Hinge moment lookup tables are integrated in the Convair 880 high fidelity flight simulation benchmark using mathematical models of energy-efficient Electro-Hydrostatic Actuators (EHA). Autopilot controls are designed for the roll, pitch, attitude and yaw damper motions using Proportional Integral (PI) controller scheduled for different flight conditions. Several different aircraft simulation scenarios are evaluated to demonstrate the efficacy and accuracy of the predicted hinge moment results.


2011 ◽  
Vol 26 (S1) ◽  
pp. s28-s29
Author(s):  
G.E.A. Khalifa

SimulationAn activity or situation that produces conditions which are not real, but have the appearance of being real, used especially for testing something. Longman Dictionary of Contemporary English. Simulation has evolved over the centuries but has not been applied to medicine until the 20th century with the introduction of virtual reality and computers. Prior to the 20th century simulation took the forms of physical models and cadavers. With the introduction of flight simulation there was an effort to move similar approaches into medicine. This was pushed by the demands of minimally invasive surgery and the introduction of robotics in surgery. In the 21st century in addition to cognitive task analysis tools we are beginning to see the migration of advanced intelligence tools to simulation. We are just at the beginning of how we will use adversarial reasoning in the medical environment and in high risk time constrained situations like Emergency Medicine. The practitioner of emergency medicine is at high risk for errors because of multiple factors including high decision density, high levels of diagnostic uncertainty, high patient acuity, and frequent distractions. Some authors have suggested that instructing physicians in “cognitive forcing strategies” or “metacognition” will help reduce the amount of cognitive error in medical practice. It has been said ‘‘[There is an] ethical obligation to make all efforts to expose health professionals to clinical challenges that can be reasonably well simulated prior to allowing them to encounter and be responsible for similar real-life challenges.’' TYPES OF SIMULATION • Verbal • Tactile • Visual • Situational • Environmental TYPES OF SIMULATION TRAINING • Standardized patients (role play) • Basic models (partial task trainers) • Simple level • Higher level • Mannequins • Low fidelity • High fidelity • Virtual patients • Screen-based; computer-based • COMBINATIONS • Augmented sp encounters with technology • Crises management HUMAN PATIENT SIMULATION • Realistic • Suitable for all levels • Safe • Wide variety of training programs • Expensive ADVANTAGES OF SIMULATION • Patients are never at risk • Serious but infrequent events, in predictable times and places • Errors can be allowed to occur, and play-out • Rehearsal, repetition, mastery • Crisis management simulation, planning • Reduces institutional liability • Increases operational confidence • Produces rapid results • Allows team training • Increases institutional prestige The use of high fidelity simulations to train multidisciplinary teams in critical environments is well established.


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