1,721,156 research outputs found
Flight 4.0: The Changing Technology Landscape of Aeronautics
No full textThis chapter draws the readers into a comprehensive discussion about the advances in Information and Communication Technologies (ICT) and their influence on the technology landscape of aeronautics. It gives a rough overview of the advances in technical systems from the industrial revolution up until Industry 4.0 and elaborates the reflection of these advancements in aeronautics from the pioneers era toward Flight 4.0. It briefly describes various recent fields of research in ICT such as Cyber-Physical Systems (CPS), Internet of Things (IoT), wireless networks, multicore architectures, Service-Oriented Architecture (SOA), cloud computing, big data, and modern software engineering methodologies as the parts of future aeronautical engineering body of knowledge. Thereafter, it describes aeronautical informatics as an establishing interdisciplinary field of study of applied informatics and aeronautics
The Evolution of Simulation and its Contributions to Many Disciplines
No full textThe aims of this chapter are: (1) To provide a comprehensive view of the stages of the evolution of simulation. (2) To emphasize the phenomenal developments in many aspects of simulation which made it an important and even a vital infrastructure for many disciplines. (3) To underline the fact that the transition from
“model-based” paradigm to “simulation-based” paradigm may be beneficial for many disciplines. In Sect. 1.2, references for a systematic collection and a critical
review of about 100 definitions of simulation as well as a comprehensive and integrative definition of simulation are given. In Sect. 1.3, the reasons simulation is used are clarified. These reasons make simulation very useful for many disciplines. In Sect. 1.4, nine aspects of the evolution of simulation are clarified including simulation-based disciplines. In Sect. 1.5, many disciplines for which simulation-based paradigm would make them much more powerful and efficient are elaborated
Ontology for Objective Flight Simulator Fidelity Evaluation
No full textThe term simulator fidelity has become enormously important in the scope of simulation research, when as-sessing 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
Iterative Scenario-Based Testing in an Operational Design Domain for Artificial Intelligence Based Systems in Aviation
No full textThe use and development of Artificial Intelligence (AI) based systems is becoming increasingly prominent in different industries. The aviation industry is also gradually adopting AI-based systems, for instance, with Machine Learning algorithms for flight assistance. There are several reasons why adopting these technologies poses additional obstacles in aviation compared to other industries. One reason are the strong safety requirements which lead to obligatory and thorough assurance activities such as testing to obtain certification. Therefore, a systematic approach is needed for developing, deploying, and assessing test cases for AI-based systems in aviation. This paper proposes a method for iterative scenario-based testing for AI-based systems. The method contains three major parts: First, a high-level description of test scenarios; second, the generation and execution of these scenarios; and last, monitoring of parameters during scenario execution. Parameters are refined, and the steps are repeated iteratively. The method forms a basis for developing iterative scenario-based testing solutions. As a domain-specific example, a practical implementation of this method is illustrated. For an object detection application used on an airplane, flight scenarios, including multiple airplanes are generated from a descriptive scenario model and executed in a simulation environment. The parameters are monitored using a custom Operational Design Domain monitoring tool and refined in the process of iterative scenario generation and execution. The proposed iterative scenario-based testing method helps in generating precise test cases for AI-based systems while having a high potential for automation
AATOM - An Agent-based Airport Terminal Operations Model simulator
No full textAATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.Air Transport & OperationsEmbedded System
Advances in Software Engineering and Aeronautics
No full textAvionics, like any other safety-critical real-time systems, pose unique challenges on system design, development, and testing. Specifically, the rigorous certification process mandated for avionics software calls for additional attention. The DO-178C Software Considerations in Airborne Systems and Equipment Certification provides detailed guidelines to ensure safety measures. This chapter gives a different angle to avionics development and certification, highlighting model-based approaches for advancing the design, development, testing, and maintenance of airborne software systems. Modern software engineering processes such as agile and scrum are discussed as the new techniques in speeding up the certification hurdle, while achieving higher return on investment
50 Summers of Simulation
No full textWe are having seasons: summers and winters of many scientific disciplines. Many fields are experiencing hype cycles. Each one of us would remember “AI winter” from the history of Artificial Intelligence. Inflated expectations are followed by disappointment and eventually funding cuts. Renewing the interest takes then years if not decades. The Society for Modeling and Simulation International has achieved outstanding success in the last 50 years to keep Summer Computer Simulation Conference (SCSC) an important event through many seasons of simulation, some of which were more remarkable than others. This chapter summarizes the Panel discussion/contributions of the SCSC 2018 about the seasons in computer simulation and the ways to achieve and further prolong summers of computer simulation
Simulation-Based Engineering
No full textEngineers, mathematicians, and scientists were always interested in numerical solutions of real-world problems. The ultimate objective within nearly all engineering projects is to reach a functional design without violating any of the performance, cost, time, and safety constraints while optimizing the design with respect to one of these metrics. A good mathematical model is at the heart of each powerful engineering simulation being a key component in the design process. In this chapter, we review role of simulation in the engineering process, the historical developments of different approaches, in particular simulation of machinery and continuum problems which refers basically to the numerical solution of a set of differential equations with different initial/boundary conditions. Then, an overview of well-known methods to conduct continuum based simulations within solid mechanics, fluid mechanics and electromagnetic is given. These methods include FEM, FDM, FVM, BEM, and meshless methods. Also, a summary of multi-scale and multi-physics-based approaches are given with various examples. With constantly increasing demands of the modern age challenging the engineering development process, the future of simulations in the field hold great promise possibly with the inclusion of topics from other emerging fields. As technology matures and the quest for multi-functional systems with much higher performance increases, the complexity of problems that demand numerical methods also increases. As a result, large-scale effective computing continues to evolve allowing for efficient and practical performance evaluation and novel designs, hence the enhancement of our thorough understanding of the physics within highly complex systems
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