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In this paper, model‐based systems engineering (MBSE) and discrete event simulation (DES) are combined to assess the performance of an offshore production system at an early stage. Various systems engineering tools are applied to an industrial case concerning the retrieval of deep‐sea minerals, and a simulation engine is developed to calculate the annual production output. A mean production of 1 Million tonnes of ore per year is estimated for an operation in the Norwegian Sea using Monte Carlo simulation. Depending on the limiting design wave height of the marine operations, the estimated production output ranges from 280,000 tonnes to 1.8 Million tonnes per year. The constrained parameter of the production system is particularly the wave height operational limit of the ship‐to‐ship transfer operation. We present the learning outcome from applying MBSE and DES to this case and discuss important aspects for improved performance.

Stochastic discrete event systems play a steadily increasing role in reliability engineering and beyond in systems engineering. Designing stochastic discrete event systems presents however well-known difficulties: models are hard to debug and to validate because of the existence of infinitely many possible executions, itself due to stochastic delays, which are possibly intertwined with deterministic ones. In this article, revisiting ideas introduced in the framework of model-checking of timed and hybrid systems, we show that it is possible to abstract the time in stochastic discrete event systems. More specifically, we show that schedules of transitions can be abstracted into systems of linear inequalities and that abstract and concrete executions are bisimilar. The result presented in this article represents thus a very important step forward in quality assurance of stochastic models of complex technical systems. We illustrate the potential of the proposed approach by means of AltaRica 3.0 models.

In this article, we present a new approach of modelling epistemic uncertainties in degradation processes. This approach is established in the framework of finite degradation structures (FDSs), which is recently proposed by the authors and can be seen as a formal extension of the fault tree analysis into multistate systems. When epistemic uncertainties are added to the states of the system, it implicitly increases the number of states and make even the Boolean systems become multistate. In the existing approaches, the addition of epistemically uncertain states as well as the new valuation mappings of the operations for those states should be done manually by the analysts depending on the type of system and its components. This manual addition may be time-consuming, error-prone and lack of generality, especially when systems get large and complex. Instead of manually remodelling the system, we propose in this article to automatic transform the model built on FDSs into epistemic space to take into account epistemic uncertainties. The proposed automatic transformations are mathematically defined and explained. As results, the uncertainty-embedded (critical) scenarios and probabilistic indicators like the belief and the plausibility in the Dempter–Shafer theory can be obtained. Illustrative examples with experimental results are also provided.

As of today, most specifications of technical systems still rely on requirements written in natural language. However, this approach is known to be problem‐prone, due to the inherent ambiguity of natural languages. On the other hand, fully formal or model‐based approaches seem to be out of reach in many practical cases, especially in early design phases of systems. In this article, we study how to combine in a pragmatic way natural language requirements with models. We propose to keep both formats and to link pieces of text in requirements with elements of models. In other words, corpuses of requirements are managed as hypertexts with links to models. For this approach to be fully efficient, the text of requirements is not free, but relies on controlled natural language techniques leading to a partial structuring of the text. We show that this makes it possible to design (semi)automatic verifications on requirements and models, which would be impossible with unconstrained natural language. We illustrate here our approach on a small illustrative example and we report results obtained on a full size industrial application.

The COVID‐19 pandemic has caught many nations by surprise and has already caused millions of infections and hundreds of thousands of deaths worldwide. It has also exposed a deep crisis in modeling and exposed a lack of systems thinking by focusing mainly on only the short term and thinking of this event as only a health crisis. In this paper, authors from several of the key countries involved in COVID‐19 propose a holistic systems model that views the problem from a perspective of human society including the natural environment, human population, health system, and economic system. We model the crisis theoretically as a feedback control problem with delay, and partial controllability and observability. Using a quantitative model of the human population allows us to test different assumptions such as detection threshold, delay to take action, fraction of the population infected, effectiveness and length of confinement strategies, and impact of earlier lifting of social distancing restrictions. Each conceptual scenario is subject to 1000+ Monte‐Carlo simulations and yields both expected and surprising results. For example, we demonstrate through computational experiments that maintaining strict confinement policies for longer than 60 days may indeed be able to suppress lethality below 1% and yield the best health outcomes, but cause economic damages due to lost work that could turn out to be counterproductive in the long term. We conclude by proposing a hierarchical Computerized, Command, Control, and Communications (C4) information system and enterprise architecture for COVID‐19 with real‐time measurements and control actions taken at each level.

In this article, we propose a new approach for safety assessment of safety-critical systems. This approach, so-called Synthesis, is dedicated to the Preliminary System Safety Assessment included within the process of Safety Certification of avionic systems. The central idea consists in decomposing the assessment into two parts aiming at studying respectively the functional and the physical characteristics of the system under study. The whole approach is supported by a fully operational tool chain, dedicated to probabilistic safety assessment, which includes the AltaRica 3.0 integrated modeling environment, and a tool dedicated to the synthesis of functional minimal cutsets into physical minimal cutsets, making possible their quantitative assessment. We illustrate the benefits of the approach by means of a concrete avionic case study.

This article is a contribution to the INCOSE initiative for model‐based engineering transformation. Its material has been presented at the ALSEE tour event in Oslo in September 2016. The ideas developed here come from the practical and theoretical experience of the authors in both industrial and academic frameworks. We organize the discussion around 6 theses that aim at establishing robust conceptual foundations for the model‐based engineering transformation. We focus on model‐based systems engineering, model‐based safety assessment, and the relationship between these 2 disciplines. We report on active research initiatives that implement these 6 theses via the S2ML+X paradigm. We conclude with suggestions about future research and teaching activities.

AltaRica 3.0 is an object-oriented modelling language dedicated to probabilistic risk and safety analyses. It is a prominent representative of modelling formalisms supporting the so-called model-based approach in reliability engineering. In this article, we illustrate the key features of the AltaRica 3.0 technology by presenting the implementation of ten very common modelling patterns. We demonstrate in this way the expressive power of the language as well as its elegance and simplicity of use.

Modern industry faces new challenges making classical organization in “disciplinary silos” ill‐adapted to demands for complex and evolving systems. We advocate promoting inter‐team communication in a “Multidisciplinary Organization” can manage complexity better: it allows early problem discovery, offers solutions to more complex issues, and favors early solution emergence. Indeed the transition to such organization requires new collaborative processes and methods to integrate various engineering fields earlier and along the development cycle. In this paper, we propose a collaborative approach to set‐up adapted modeling and methodological practices in the enterprise. It leverages the rising interest for Model‐Based Systems Engineering and related modeling technologies to offer a new approach to different engineering discipline model synchronization to support their collaboration. This approach considers the studied system context (under several viewpoints), applied processes, applied methods, and viewpoints produced by engineers. We illustrate it by creating a collaboration framework between two particular engineering fields: system architecture design and avionics applicative safety analysis at different life cycle stages.

In this article, we present a scenario‐oriented modeling methodology dedicated to the analysis and the formalization of complex system specifications. The methodology relies on the SCOLA semiformal notation to describe scenarios and on a formal execution model described in the AltaRica 3.0 modeling language. We designed this methodology because we had to review thousands of pages of natural language specifications of a railway system in view of their validation with respect to safety constraints. We needed therefore some means to understand what the system was supposed to be and to do as well as to support a dialog with experts. This article aims at introducing the methodology and the modeling formalism that supports it as well as at discussing its application to the railway systems.

In discrete event simulations, the system is assumed to change of state when and only when an event occurs. This change of state can be more or less sophisticated depending on the modeling formalism. In this article, we discuss the whys and wherefores of the fixpoint assertion mechanism introduced in AltaRica 3.0 to perform changes of states. We show how it can be used to handle complex phenomena such as change in flow directions depending on the states of components. We propose an efficient implementation of this mechanism, thanks to ideas stemmed in theoretical computer science and artificial intelligence. We compare the AltaRica 3.0 approach with alternative ones, including those of the previous versions of the language.

In this article, we present an algorithm to generate minimal cut sets from AltaRica 3.0 models. AltaRica 3.0 improves the previous versions of the language by introducing a fixpoint mechanism to stabilise values of variables after each transition firing. This fixpoint mechanism allows the design of acausal models and the analysis of systems with instant loops. It makes however the generation of fault trees more complex. We show here that by using advanced partitioning techniques, we can nevertheless design an efficient generation algorithm. We illustrate the different steps of this algorithm by means of a red wire example.

Importance factors are indicators of the risk significance of the components of a system. They are widely used in probabilistic safety analyses to rank components according to their contribution to the global risk. In this article, we review definitions and interpretations of importance factors in the case the support model is a coherent fault tree, and failures of components are described by basic events of that fault tree. First, we show that each importance factor characterizes the probability of a certain set of minterms. The notion of critical states, that is, minterms in which failing/repairing the component suffices to fail/repair the system, plays a central role in this process. Then, we discuss assessment algorithms for the two main technologies at hand: minimal cutsets and binary decision diagrams. Finally, we draw some practical conclusions from these developments. This article thus contributes to clarify mathematical and algorithmic foundations of importance factors.

Assessing the reliability of systems with mobile components, that is components whose locations and interactions change during the mission of the system, raises a number of specific modeling issues. In this article, we compare two candidate modeling formalisms to do so: AltaRica and PEPA nets. We study their respective advantages and drawbacks and we show benefits of a cross fertilization.

Two well-known modelling approaches are in use in probabilistic risk assessment: fault tree linking and event tree linking. The question of which modelling approach is most appropriate for specific applications has been extensively, if not emotionally, debated among experts in the past two decades, addressing both modelling and quantification issues. In this article, we determine their degree of equivalence and build ‘methodological bridges’ between the two approaches from a mathematical and algorithmic perspective. We show that, under certain conditions, both modelling approaches are equivalent. Since both fault tree linking and event tree linking approaches are subject to limitations and approximations, established bridges make it possible to formulate important recommendations for probabilistic risk assessment practitioners and quantification engine developers.

In this paper, we report experiments we did with the constraint languageToupie. Toupie is a finite-domain µ-calculus interpreter. In additionto classical functionalities of a finite-domain constraint solver, itprovides a full universal quantification, and relations(predicates/constraints) can be defined as least or greatest fixpoints ofequations. This expressiveness is coupled with a practical efficiency thatcomes from the management of relations via decision diagrams. We advocatethe use of this paradigm to model and solve efficiently difficult constraintproblems such as the computation of properties of finite-state machines andthe implementation of abstract interpretation algorithms for logic languages.

In this paper, we are interested in the problem of prescribing the scalar curvature on a compact riemannian manifold of negative conformal invariant. We give a necessary and sufficient condition when the prescribed function $ f$ 0$ --> 0$">, we merely find a sufficient condition. This is the subject of the first theorem. In the second one, we prove the multiplicity of the solutions of subcritical (for the Sobolev imbeddings) elliptic equations. In another article [8], we will prove the multiplicity of the solutions of the prescribing curvature problem, i.e. for a critical elliptic equation.