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International audience; In the beginning of 2022, the world is still fighting the crisis caused by the COVID-19 outbreak. The scientific community is still dedicating significant efforts to identify which are the better strategies to mitigate the pandemic and establish how and when to apply them. Modeling and simulation are a common method to replicate and foresee the behavior of the epidemic curve, but traditional analytical models are not capable to explain and reproduce the real evolution of the number of infections and deaths as they only concentrate in the epidemiological aspects of the virus. The COVID-19 crisis has an impact in all fundamental levels of society, and this is the reason why its modeling requires a global perspective and a holistic approach. Though the engineering scope is not common in the study of public health crises, this paper concludes that some engineering tools such as systems analysis and control theory may be the answer to build a high-fidelity model to support the decision-making facing the emergency.

Over the past two decades, the promoters of Model Based Systems Engineering (MBSE) have encouraged systems engineers to transition from document-centric approaches to model-based ones. Literacy of systems engineers in reading, sharing and elaborating models has therefore become an issue. Whatever the modeling language, elaboration of models is a highly complex intellectual process and SysML is no exception. Feedback from industry practitioners and MBSE lecturers suggests that developers of SysML models often stumble on the same problem: thinking about the system before modeling it in SysML. The authors of this paper propose to ease that elaboration process by using mind maps. With their graphic form and rather flexible way of organizing ideas, mind maps turn out to be a good candidate to help thinking about the system. Unlike approaches that directly switch from mind maps to SysML diagrams dedicated to one specific system, this paper introduces an intermediate step: mind maps first enable elaboration of diagrams patterns. The latter may in turn be instantiated onto one or several systems. Without loss of generality, the proposed approach is step-wise illustrated on real-time systems monitored by software controllers. Patterns are proposed to cover need expression, requirement capture, use case driven analysis and design.

A prominent pathway for the aeronautical industry to meet contemporary challenges is to explore overall vehicular efficiency gains enabled by various functional and structural distributions and/or synergies between onboard systems. To that end, this paper combines analytical first-principle-based methods and principles of systems engineering and focuses on the Environmental Control System (ECS). The objective is to develop means for simple blank-sheet design of complete system architectures, which would help unlock potentially obscured parts of the system design space. Basic thermodynamics is employed, complemented with “Function- Behaviour-Structure-Experience” systems engineering framework. The method presented in the paper enables users to initialise the design from a primitive abstract system architecture described by elementary physical processes, and then carry out a sequence of decisions and design material systems architecture, i.e. concepts that respond to the system requirements. The preliminary results present development of architectures representative both of traditional pneumatic and innovative electrical ECS concepts. Energy consumption figures of merit (thermodynamic efficiency, exergy destruction rate) are used as guidelines during the design i.e. for a given flight condition, the designer can assess the influence of each choice on the overall system energy consumption. Trade-offs between architectural design choices and figures of merit are thus rendered transparent in preliminary architecture design. In this paper the figures of merit are based on thermodynamic energy efficiency; in perspectives the method can include other constraints such as e.g. weight, volume, cost, or other, with long-term objective of enabling a comprehensive multi-disciplinary multi-system aeroplane architecture design scheme.

This paper proposes a novel model-based approach to technology planning and roadmapping, consisting of two complementary steps: technology forecasting and game-theoretic planning. The inherent uncertainty of target technology performances, timelines and risks impact the roadmapping process. Reducing this uncertainty is a major challenge and allows elaborating different options and scenarios. A formal methodology is proposed for quantitative forecasting in a multi-dimensional space (different performance metrics and time) based on past technology development trend information. The method adopts concepts and approaches from econometrics and is formulated as a convex optimization problem with different constraints on the frontier’s shape. It provides useful product line assessment benchmarks and helps to set reasonable goals for future technology developments. Game-theoretic planning allows addressing the strategic decisions to take, considering the technology land-scape, markets, and competition. The strategic decisions affect in turn other companies as well, which is the basis for the application of game theory, in the form of best-response functions to determine the subsequent reactions and movements of rivals in a technological landscape. The result is a simulation of a sequential game in tech-nology space, allowing evaluating possible technological development pathways and determining optimal models on the Pareto frontiers, potential targets for technology roadmapping.