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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

Complex systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and highly integrated as prescribed by the SAE ARP 4754 Standard. They integrate many technologies and they work in very demanding environments sometimes with little or no maintenance due to the severe conditions of operation. To survive such harsh operating conditions, they require very high levels of reliability, to be reached by a diversity of approaches, processes, components, etc. By their turn, the processes of analysis and decision making shall be improved progressively, as experience accumulates and suggests modifications and adaptations. According to this philosophy, in this work, we discuss a proposal for improving the results of the Reliability Analysis and FMEA/FMECA of the CBERS Satellite Program, conducted at the National Institute for Space Research-INPE, since 1987.

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

Systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and highly integrated, as prescribed by the SAE ARP 4754 Standard. They integrate many technologies and they work in very demanding environments, sometimes with little or no maintenance, due to the severe conditions of operation. To survive such harsh operating conditions, they require very high levels of reliability, to be reached by a diversity of approaches, processes, components, etc. By their turn, the processes of analysis and decision making shall be improved progressively, as experience accumulates and suggests modifications. Most of this can be translated in models. According to this philosophy, in this work, we discuss the use of Model Based Reliability for improving the results of the Reliability Analysis and FMEA/FMECA of a satellite program, as those conducted at the National Institute for Space Research-INPE, since 1979.

Currently, the use of Global Navigation Satellite Systems-GNSS has been widely disseminated for the most different applications, from the aeronautical navigation to the car traffic, being the Global Positioning System-GPS the most used system for such objectives. New applications have presented challenges in terms of the main requirements associated to such systems, namely: precision, reliability, availability, continuity and integrity. It is because proposed solutions, such as satellite or ground-based augmentation systems, depend on signals provided by the GNSS satellite constellation. It constitutes a limitation for using such systems for position and velocity estimations. On other hand, Inertial Navigation Systems-INS, being independent of external signals, have a big potential to be applied on these circumstances; furthermore, they present characteristics that may be considered complementary to the GNSS.

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.

Systems such as satellites, aircrafts, automobiles and air traffic controls are becoming increasingly complex and/or highly integrated, as prescribed by the standard SAE-ARP 4754A Standard. They integrate many technologies and they work in very demanding environments, sometimes with little or no maintenance, due to the severe conditions of operation. To survive such harsh operating conditions, they require very high levels of dependability, to be reached by a diversity of approaches, processes, components, etc. Some are suggested by the SAE-ARP-4754A as one of the highest level standards to be met. So, it is important to know it and its consequences for product and staff deeply. The aim of this paper is to present: a discussion on the standard SAE-ARP-4754A and a proposal for using it in product certification and qualification of staff.

The measure of time intervals with relatively high accuracy (of 1 milisecond, at least) in PC computers is a relatively hard task to solve. But this is essential for the digital simulation, with sensors in the loop, of fast control systems. This work allows the reading of the programmable internal timer 8253 present in a typical PC, reaching 1 ms resolution, at least, through a C high level language routine. The determination of the angular velocity of a 53M2-30H Contraves 3-axis dynamic simulator used in that simulation was improved by the use of this work, allowing the acquisition of consecutive measures of angles and angular velocities with a time interval smaller than 10 ms in some cases. Using this routine and other simulator control and monitoring softwares we estimated the angular velocity faster (100 ms × 210 ms)and better than the simulator Rate Readout Module, and used it in a fast real time control simulation.

Resistance Spot Welding is a manufacturing process widely used in several industrial segments, such as automotive, electronics, aerospace and others. It stands out from other welding processes, as it does not require addition material to join parts. This type of process needs to be robust and reliable in order to ensure the quality of the welded joint produced, as any variation in the quality of the weld point can affect the functionality and safety of the final product. The resistance spot welding process uses different technologies and operating sequences that depend on various characteristics, factors and parameters. The combinations and values of these allow for numerous possibilities, making their adjustments time-consuming, costly and exhaustive, so it is necessary to apply statistical techniques to optimize the process. In the literature, it is possible to find several statistical techniques for the optimization of the process.

In this work we discuss the fault avoidance and the fault tolerance approaches for increasing the reliability of aerospace and automotive systems. This includes: the basic definitions/concepts (reliability, maintainability, availability, redundancy, etc.), and characteristics (a priori analysis, a posteriori analysis, physical/hardware redundancy, analytical/software redundancy, etc.) of both approaches, their mathematical background and models (exponential, Weilbull, etc.), their basic theory, their methods and techniques (fault trees, dependence diagrams, Markov chains, etc.), some of their standards (SAE-ARP4761, AC 25.1309, etc.) and simulation environments (Cafta, etc.), and their applications to the reliability analysis and reliability improvement of aerospace and automotive vehicles. This is illustrated by some examples driven from the aerospace and automotive industries.