Ontology for Objective Flight Simulator Fidelity Evaluation

Abstract

The term simulator fidelity has become enormously important in the scope of simulation research, when assessing training efficiency and the transfer of training to real flight. It is defined as the degree to which a flight simulator matches the characteristics of the real aircraft. Objective simulator fidelity provides an engineering standard, by attacking the fidelity problem with comparison of simulator and the actual flight over some quantitative cues. Research flight simulation encompasses some differences from commercial flight simulation. It requires high flexibility and versatility concerning the cockpit layout and visual and motion systems, as well as flight simulation models. It shoud be easy to modify the flight simulation model or other soft-and hardware components of the simulator. By this, there is a need for an automatic test method, in order to determine the fidelity of the most relevant simulator subsystems, since they are often modified during the life cycle of the simulator. The Institute of Flight Systems (FT) at the German Aerospace Center (DLR) has a reconfigurable flight simulator, the Air Vehicle Simulator (AVES), for research of rotorcraft and fixed-wing aircraft. The study reported in this paper targets a model based testing approach designed to tackle the high flexibility requirement of AVES. This paper presents a metamodel for objective flight simulator evaluation. Metamodeling has been carried out in two levels. An Experimental Frame Ontology (EFO) has been developed adopting experimental frames from Discrete Event System Specification (DEVS), and as an upper ontology to specify a formal structure for simulation test. Then in Objective Fidelity Evaluation Ontology (OFEO) that builds upon EFO, domain specific meta-test definitions are captured.

The process of building the upper-level hierarchy for the aircraft structure ontology to be integrated in FunGramKB

“…Reiss et al (2006) refer to the phases-of-flight described by Travers (2000) to enable a user-oriented description of the fragmented sequential phases a flight crew must go through for the accomplishment of a mission. However, the amount of existing taxonomies and ontologies in aeronautics is extremely diverse, as it is the case of route planning (Liu et al, 2009, Niaraki & Kim, 2009, traffic flow management (van Putten et al, 2008), flight simulator fidelity evaluation (Durak et al, 2014), aircraft assembly (Hongjung et al, 2007), aircraft fault knowledge (Yi et al, 2009, Zhou & Li, 2011 or maintenance task support (Brusilovsky & Cooper, 2002, Wu et al, 2014, among others.…”

Model-Based Testing for Objective Fidelity Evaluation of Engineering and Research Flight Simulators

“…To apply the proposed approach for objective validation, based on our ontology for objective flight simulator fidelity evaluation, 17 an SES has been constructed. A simplified excerpt from this SES has been depicted in Fig.…”

“…Based on the SES and MB, a specific executable test case or a test suite is automatically generated for a flight simulation model under test. The specification of objective fidelity evaluation test cases in SES ontology are mostly adopted from Objective Fidelity Evaluation Ontology of Durak et al 17 Utilizing the testing infrastructure from Schmidt et al 14 , implementation is carried out in MATLAB/Simulink; BMs are developed as Simulink block and SES is described using SES Toolbox for MATLAB/Simulink. 18 The approach is exemplified with the construction of a sample test suite.…”

Usability evaluation of a web-based ontology browser: the case of TSONT

“…Accordingly, the researchers determined the tasks and questions to be asked before and after implementation in order to assess browser usability. Related tasks were created in order to emphasize effectiveness [3] and efficiency [32]. Based on [26]'s usability metrics, [32]'s task creation standards included overview (i.e.…”

“…This vocabulary includes definitions and indications of how concepts are interrelated, which, collectively, imposes a structure on the domain and constrains possible interpretations of the terms [2]. An ontology includes taxonomies, which are widely used to capture generalizations, and specializations related to a domain [3]. Knowledge in ontology is formalized using five types of components: concepts, relations, functions, axioms, and instances [1].…”