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In this work we discuss some types of simulation architectures and standards, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity x fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), their standards (OMBA, SIMNET, ALSP, DIS, HLA 1.3, HLA 1516, ASIA, AP2633, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

In this work we discuss some types of simulators and simulations, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“standalone”, PIL, HIL, MIL, DIS, HLA, etc.), their environments (discrete, continuous, hybrid, etc.) and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry

In this work we discuss current trends driving the aerospace and automotive systems architectures. This includes trends as: 1) pos-globalization and regionalization; 2) the formation of knowledge oligopolies; 3) commonality, standardization and even synergy (of components, tools, development process, certification agents, standards); 4) reuse and scalability; 5) synergy of knowledge and tools convergence; 6) time, cost and quality pressures and innovation speed; 7) environmental and safety issues; and 8) abundance of new technologies versus scarcity of skilled manpower to apply them.

In this work we discuss some types of simulation environments and laboratories, their characteristics and applications to the simulation and control of aerospace vehicles. This includes: the basic definitions, types and characteristics of simulators and simulations (physical, computational, hybrid, etc.; discrete events, discrete time, continuous time, etc; deterministic, stochastic, etc.) their basic compromise (simplicity × fidelity), their man-machine interfaces and interactions (virtual, constructive, live, etc.), their evolution law (time, events, mixed, etc.), their architectures (“stand-alone”, PIL, HIL, MIL, DIS, HLA, etc.), and especially, their environments (discrete, continuous, hybrid, etc.) and laboratories (physical, computational, hybrid, etc.), and their applications to the simulation and control of aerospace vehicles. This is illustrated by some examples driven from the aerospace industry.