A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

A systems thinking approach for project vulnerability management

International audiencePurpose: This papers aims at developing the concept of project vulnerability in order to focus on the weaknesses of a project system, instead of focusing on risk evaluation only. We then aim at concentrating on a systems thinking based view to highlight the potentially endangered elements of a project, including its outcomes.Design/methodology/approach:•A broad state of the art in many scientific domains.•A definition of project vulnerability.•A description of a project vulnerability management process, including identification, analysis and response plan.•A test on an industrial case studyFindings: Our project vulnerability management process permits to concentrate directly on the existing weaknesses of a project system which may create potential damages regarding the project values creation. By focusing on this system, response plans may be more adapted to the existing lacks of the project. Research limitations/implications: Some aspects of the vulnerability definition should be refined, like the concepts of susceptibility or cruciality. Other promising works may focus on the evaluation of the non-resistance and resilience, notably thanks to the introduction of interdependences which exist in complex projects.Practical implications: A case study was done on a decision support system (called FabACT) developed at Hôpital Européen Georges Pompidou Pharmacy department. The aim of this project was to achieve a better balance between the workload and the efficiency of the compounding unit. Originality/value: This article presents an innovative way to analyse a project’s vulnerability by focusing on its existing weaknesses using a systems thinking-based approach

Managing Complex, High Risk Projects: A Guide to Basic and Advanced Project Management

International audienceMaximizing reader insights into project management and handling complexity-driven risks, this book explores propagation effects, non-linear consequences, loops, and the emergence of positive properties that may occur over the course of a project.This book presents an introduction to project management and analysis of traditional project management approaches and their limits regarding complexity. It also includes overviews of recent research works about project complexity modelling and management as well as project complexity-driven issues. Moreover, the authors propose their own new approaches, new methodologies and new tools which may be used by project managers and/or researchers and/or students in the management of their projects. These new elements include project complexity definitions and frameworks, multi-criteria approaches for project complexity measurement, advanced methodologies for project management (propagation studies to anticipate potential behaviour of the project, and clustering approaches to improve coordination between project actors) and industrial case studies (automotive industry, civil engineering, railroad industry, performing arts,…) and exercises (with their solutions) which will allow readers to improve and strengthen their knowledge and skills in the management of complex and (thus) risky projects

Forming risk clusters in projects to improve coordination between risk owners

International audienceDue to the growing complexity of projects, their risks have increased in number and criticality. Risk lists thus need to be broken down into smaller, more manageable clusters. Classical clustering techniques are generally based on a single parameter, like risk nature, criticality or ownership. Risk interactions are therefore not properly considered when building up clusters. That is why this paper aims at grouping risks so that the com-munication and coordination between the actors who are committed in the management of the project and its risks are facilitated. The work is based on an optimization algorithm which maximizes interaction rate within the risk clusters. This paper focuses on two additional points. First, the optimization problem formulation is enriched by some constraints related to the risk owners, not only to the risks. Second, a frequency approach is introduced, to test different configurations, in order to improve the robustness of the clustering decision. It enables meaningful and operationally realistic actors groups to be organized, regarding not only the interaction rate between risks but also the relationships between risk owners. Our clustering approach encourages people to meet together and communicate/ coordinate better, which we hope will contribute to prevent some unde-sired complex phenomen

Understanding project complexity: implications on project management

International audiencePurpose: Better identify, define and model complexity within the field of project management in order to manage better under conditions of complexity (and manage better complexity-induced risks). Design / Methodology / Approach: A large literature review enlightens the lack of consensus on project complexity and thus provides a broad view and a critical analysis of the underlying concepts. A project complexity framework and definition are then proposed. After underlining the stakes of project complexity in accordance with these proposals, a project complexity model is then built notably thanks to systems analysis.Findings: Proposal of standard project complexity framework and definition. Proposal of a synthesis of the relationships between the concepts of project uncertainty and project complexity. Proposal of a project complexity model (and validation thanks to industrial application).Research limitations / implications: The literature review and project complexity framework tries to be exhaustive even though it is likely to be completed. The final version of the model is still to be computed and tested.Practical implications: Avoid confusion when defining and managing a complex project, particularly between project team members (and as a consequence improve communication and information sharing). Better assess the propagation of a change within the project. Originality / Value: The paper proposes an original framework and definition of project complexity. The complexity model permits the navigation from any element of the project to any other (when detail is needed) and is as a consequence original and complementary with traditional project management models and tools

Project risk management processes: improving coordination using a clustering approach

International audienceProjects are dealing with bigger stakes and facing an ever-growing complexity. In the first place, project risks have increased in number and criticality. Lists of identified project risks thus need to be broken down into more manageable clusters. Existing techniques for this are generally based on a well-known parameter such as the nature of the risk or its ownership. The limits of this approach are that project risk interactions are not properly considered. Project interdependent risks are thus often analysed and managed as if they were independent. The consequence is that there may be a lack of consideration of potential propagation through this risk network. A change may have dramatic consequences if the propagation chain is not clearly identified and/or not managed. Our objective in this paper is to propose a methodology for grouping risks so that the project risk interaction rate is maximal inside clusters and minimal outside. What we hope to achieve is a method which facilitates the coordination of complex projects which have many interrelated risks with many different risk owners. We contend that the capacity of risk owners to communicate and make coordinated decisions will be improved if they are grouped in such a way. This proposed reconfiguration of organisation is complementary to existing configurations. To do this, we first model project risk interactions through matrix representations. Then, the mathematical formulation of the problem is presented and two heuristics are introduced. A case study in the civil engineering industry (a large infrastructure public-private partnership project) is presented, which enables us to propose global recommendations, conclusions and perspectives