PROJECT TITLE : Interdisciplinary study on the mitigation of NaTech risks in a complex world: learning from Japan experience applying ERRA NaTech, iNTeg-Risk project KEYWORDS : Tsunami ; Domino effect ; Collecting data ; Risk analysis ; Mechanical behaviour of equipments; Equipments reliability ; Warning systems ; Land use planning ; Supply chain ; Economic impact ; SUMMARY : The great earthquake that occurred on March 11th in Japan, is one of the three most powerful earthquakes in the world overall since modern record-keeping began in 1900. The earthquake triggered a destructive tsunami that hit the Pacific Coast of Japan, especially the Tohoku region. This event showed that combination between natural and technological hazards within industrial plants could have disastrous consequences on installations containing hazardous substances, electrical systems, etc. Taking into account the climate change and growing urbanisation on areas prone to natural hazards, the likelihood of natural disasters will increase with a higher exposure of the population. As a consequence, technological accidents triggered by natural disasters (NaTech accidents) pose an enormous challenge to the stakeholders of industrial sector. An answer to this emerging risk has been provided in the framework of iNTeg-Risk, with the development of a specific risk analysis tool ERRA NaTech. This methodology, based on assumptions and simplifications from multi-disciplinary expertises, aims to define measures to prevent Natech accidents within industrial areas. The project presented below, consists in applying the methodology ERRA NaTech in the context of the great Tohoku earthquake. The implementation of methodologies, to define risk prevention measures, requires learning lessons from the feedback of natural disasters. Thus, this disaster in Japan is an opportunity to develop our currents methodology, in particular to tsunami hazard. Such a project involves generating new methods in various engineering fields: risk analysis, structural resistance, uncertainties, and reliability. The earthquake in Japan has proved that engineering tools had to be completed by a policy approach. For instance, the experience has revealed that implementation of emergency measures dedicated to tsunami within industrial sites is an efficient mean to prevent equipment failure and domino effects. Finally, the Japan disaster has shown the vulnerability of the economic system trough the supply chain disruption and its consequences on non impacted areas. Higher dependency on vulnerable networks (telecom, lifelines, and installations) is the main explanation to this failure. The complexity of a multi-disciplinary analysis as Natech risk assessment requires regrouping various expertises. That’s the reason why partners involved in this project have a complementary approach starting from engineering expertise to social sciences.

INFRASTAR aims to develop knowledge, expertise and skill for optimal and reliable management of structures. The generic methodology will be applied to bridges and wind turbines in relation to fatigue offering the opportunity to deal with complementary notions (such as old and new asset management, unique and similar structures, wind and traffic actions) while addressing 3 major challenges: 1/advanced modelling of concrete fatigue behaviour, 2/new non destructive testing methods for early aged damage detection and 3/probabilistic approach of structure reliability under fatigue. Benefit of cross-experience and inter-disciplinary synergies will create new knowledge. INFRASTAR proposes innovative solutions for civil infrastructure asset management so that young scientists will acquire a high employment profile in close dialogue between industry and academic partners. Modern engineering methods, including probabilistic approaches, risk and reliability assessment tools, will take into account the effective structural behaviour of existing bridges and wind turbines by exploiting monitored data. Existing methods and current state-of -the art is based on excessive conservatism which produces high costs and hinders sustainability. INFRASTAR will improve knowledge for optimising the design of new structures, for more realistic verification of structural safety and more accurate prediction of future lifetime of the existing structures. That is a challenge for a sustainable development because it reduces building material and energy consumption as well as CO2 production. Within the global framework of optimal infrastructure asset management, INFRASTAR will result in a multi-disciplinary body of knowledge covering generic problems from the design stage process of the new civil infrastructures up to recycling after dismantlement. This approach and the proposed methods and tools are new and will allow a step forward for innovative and effective process.

The ASSAS project aims at developing a proof-of-concept SA (severe accident) simulator based on ASTEC (Accident Source Term Evaluation Code). The prototype basic-principle simulator will model a simplified generic Western-type pressurized light water reactor (PWR). It will have a graphical user interface to control the simulation and visualize the results. It will run in real-time and even much faster for some phases of the accident. The prototype will be able to show the main phenomena occurring during a SA, including in-vessel and ex-vessel phases. It is meant to train students, nuclear energy professionals and non-specialists. In addition to its direct use, the prototype will demonstrate the feasibility of developing different types of fast-running SA simulators, while keeping the accuracy of the underlying physical models. Thus, different computational solutions will be explored in parallel. Code optimisation and parallelisation will be implemented. Beside these reliable techniques, different machine-learning methods will be tested to develop fast surrogate models. This alternate path is riskier, but it could drastically enhance the performances of the code. A comprehensive review of ASTEC's structure and available algorithms will be performed to define the most relevant modelling strategies, which may include the replacement of specific calculations steps, entire modules of ASTEC or more global surrogate models. Solutions will be explored to extend the models developed for the PWR simulator to other reactor types and SA codes. The training data-base of SA sequences used for machine-learning will be made openly available. Developing an enhanced version of ASTEC and interfacing it with a commercial simulation environment will make it possible for the industry to develop engineering and full-scale simulators in the future. These can be used to design SA management guidelines, to develop new safety systems and to train operators to use them.

As technology increases and performance requirements continually tighten, the cost and the required precision of assemblies increase as well. Due to the variations associated with manufacturing process, it is not possible to attain the theoretical dimensions in a repetitive manner. It causes a degradation of functional characteristics of the product. In order to ensure the desired behavior and the functional requirements of the system in spite of variations, the component features are assigned a tolerance zone within which the value of the feature i.e. situation and intrinsic lie. Therefore, tolerance analysis is a key element in industry for improving product quality and decreasing the manufacturing cost. In addition, it participates to an eco-aware attitude since it allows industrials to manage and reduce scrap in production. Tolerance analysis concerns the verification of the value of functional requirements after tolerance has been specified on each component. The main objective of the AHTOLA proposal is to develop hybrid approaches for a large scope of product behavior models. Those approaches have to be based on: - worst case analysis approaches like Solution Space Exploration based on Interval Reduction Methods (Numerical Quantified Constraint Satisfaction Problem - Box consistency, …), Solution Space Exploration based on Evolutionary Methods (Genetic algorithm, …), and … to assess the worst gap configurations regarding to the assemblability and the functional requirements ; - probabilistic approaches like Simulation based method (Monte Carlo Simulation …), Most probable point based methods (FORM-SORM), Meta-modeling based method (Kriging …), to estimate the probability of system conformity based on the process capabilities or statistical distributions of component deviations.