Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲ Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5341 Conference paper 2021 Weppe A.; Bony-Dandrieux A.; Tixier J.; Chapurlat V.; Kamissoko D.; Daclin N. An innovative approach for ongoing assessment of critical infrastructures’ resilience based on a nonfunctional requirement ecosystem Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021 10.3850/978-981-18-2016-8_377-cd Geopolitical context or climate change induced more and more disasters in the two last decades. Particularly, Critical Infrastructures (CI-e.g., water distribution, health care) that support the daily life of societies are impacted by these disasters. These CI are indeed essential. By their various interactions and links, they become more fragile when facing complex situations. For instance, a local event, occurring in a CI (e.g., an accident), can propagate throughout these interactions, impacting other CI, leading to a higher intensity and to a global impact. Classical risks analysis is limited in terms of global and dynamic vision of these CI, to manage these events efficiently and to recover to an acceptable functioning state for the end users. To this purpose, resilience is a useful concept, highlighted by numerous research works and organizations to characterize the best way a CI has to react to an undesirable event and avoid, if possible, its propagation. The purpose of this paper is to present the main principles of a methodology to assess and analyze resilience of a CI based on a multi views and systemic model formalized as a digital twin. This work is done in the frame of the project RESIIST supported by the French research agency ANR (Résilience des infrastructures et systèmes interconnectés, 18-CE39-0018-05) to provide scenarios to test and evaluate the proposed methodology. © ESREL 2021. Published by Research Publishing, Singapore.
publications-5342 Article 2021 Xu Z.; Dai C.; Wang J.; Liu L.; Jiang L. Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network Advances in Civil Engineering 10.1155/2021/3918524 In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions. Β© 2021 Zhonghua Xu et al.
publications-5343 Article 2021 He Y.-X.; Liang Y.; Xiong Y.; Mou J.-M.; Li M.-X.; Zhang K. Dynamic adaptive intelligent navigation method for multi-object situation in open water; [开阔水ε_x009f__x009f_ε¤_x009a_η‰©ζ ‡ε_x008a_¨ζ€θ‡适应智能θˆθ΅_x008c_方法] Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering 10.19818/j.cnki.1671-1637.2021.05.025 An intelligent navigation method of dynamic adaptive target ship collision avoidance action in open water was proposed considering the ship maneuvering characteristics, the requirements of International Regulations for Preventing Collisions at Sea 1972 and good seamanship. The digital twin traffic environment was constructed by classifying and modeling objects. An automatic navigation model was developed by combination of course control method, ship maneuvering motion and sailing resuming model, and ship's nonlinear maneuvering motion was deduced. The requirements of International Regulations for Preventing Collisions at Sea 1972 were quantitatively analyzed based on the automatic navigation model, and the dynamic collision avoidance mechanism was studied. The method to calculate applicable course was established. In the multi-target environment, the maneuvering discrimination method of target ship was proposed. The method to obtain the factors such as the course changing time, amplitude and sailing resuming time which constitute the autonomous navigation scheme under the constraint of rules was studied. Simulation results show that the intelligent navigation method can adapt to the residual error and random motion of the target ships based on the rolling calculations of the information update at the second-level. The proposed intelligent navigation method can accurately achieve the feasible course range and course change amplitude of 1Β°. The calculation step lengths of program and sailing resumption time are set to 1 and 10 s, respectively, and multiple static objects and six target ships maintaining the course and speed are established in this simulation environment. Own ship remains clear from all targets and sails autonomously to the destination after a series of maneuverings of 9Β° to starboard, sailing resuming, keeping course and speed, sailing resuming at 640, 1 053, 2 561 and 3 489 s, respectively. Target ships are set to perform uncoordinated collision avoidance actions at 300 s, and own ship remains clear from all targets and sails autonomously to the destination after a series of maneuverings of 9Β° to starboard, 12Β° to port, 17Β° to starboard, and sailing resuming at 980, 2 790, 3 622 and 5 470 s, respectively. Therefore, a ship in any initial states can automatically sail along a planned route to its destination. The proposed method is suitable for intelligent navigation in actual open sea areas with multiple and multiple dynamic and static objects. Β© 2021, Editorial Department of Journal of Traffic and Transportation Engineering. All right reserved.
publications-5344 Article 2020 Pedersen T.A.; Glomsrud J.A.; Ruud E.-L.; Simonsen A.; Sandrib J.; Eriksen B.-O.H. Towards simulation-based verification of autonomous navigation systems Safety Science 10.1016/j.ssci.2020.104799 Autonomous ships are expected to change water-based transport of both cargo and people, and large investments are being made internationally. There are many reasons for such transformation and interest, including shifting transport of goods from road to sea, reducing ship manning costs, reduced dangerous exposure for crew, and reduced environmental impact. Situational awareness (SA) systems and Autonomous navigation systems (ANS) are key elements of autonomous ships. Safe deployment of ANS will not be feasible based on real-life testing only, but will require large-scale, systematic simulation-based testing in addition to assurance of the development process. DNV GL proposes to use a digital twin, meaning a digital representation of key elements of the autonomous ship as a key tool for simulation-based testing. The digital twin contains comprehensive mathematical models of the ship and its equipment, including all sensors and actuators. The complete simulation-based test system complementing the digital twin should consist of a virtual world to simulate environmental conditions, geographical information and interaction with other maritime traffic. Finally, the test system must include a test management system that controls simulation of the digital twin and the virtual world, generates test scenarios as well as evaluates the test scenario results. An automatic scenario generation tool should search for low ANS performance, and ultimately establish sufficient coverage of the possible scenario space. The test scenario evaluation should automatically consider safety, conformance to collision regulations at sea (COLREG), and possibly also the efficiency of the ship navigation. This paper presents a comprehensive prototype of a test system for ANS. Key topics are simulation-based testing, interfacing the simulator and ANS, cooperation with ANS manufacturers, dynamic test scenario generation, automatic assessment towards COLREG and experiences from the cooperation with The Norwegian Defense Establishment (FFI). Β© 2020 Elsevier Ltd
publications-5345 Article 2021 Seo D.; Huh T.; Kim M.; Hwang J.; Jung D. Prediction of air pressure change inside the chamber of an oscillating water column–wave energy converter using machine-learning in big data platform Energies 10.3390/en14112982 Wave power is an eco-friendly power generation method. Owing to the highly volatile nature of wave energy, the application of prediction techniques for power generation, failure diagnosis, and operational efficiency plays a key role in the successful operation of wave power plants (WPPs). To this end, we propose the following approaches: (i) deriving the correlation between highly volatile data such as wave height data and sensor data in an oscillating water column (OWC) chamber; (ii) development of an optimal training model capable of accurate prediction of the state of the wave energy converter (WEC) based on the collected sensor data. In this study, we developed a big data analysis system that can utilize the machine learning framework in KNIME (an open analysis platform), and to enable smart operation, we designed a training model using a digital twin of an OWC–WEC that is currently in operation. Using various machine learning models, the pressure of the OWC chamber was predicted, and the results obtained were tested and evaluated to confirm its validity. Furthermore, the prediction performance was comparatively analyzed, demonstrating the excellent performance of the proposed CNN-LSTM-based prediction model. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publications-5346 Conference paper 2021 Gomes P.J.; Cao F.; Hanzon L.; Ogugbue C.E.; Bratley K.; Dumenil J.-C.; Slatcher T.; Floren A.; Walker G.; Kontogiannis G. Digital-Twin for Production Monitoring and Optimisation: Two Case Study Application Examples Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 10.2118/208104-MS Well network simulation and optimization is an established technology within BP for production optimization. However, for simplicity, the processing facilities are usually only considered as fixed oil, gas and water flow rate constraints. Actual production limits vary as a function of operating conditions and/or cannot be measured directly (e.g. True Vapour Pressure (TVP) or gas velocity at the inlet separator nozzles). To improve on existing workflows, BP has expanded its existing petroleum engineering-focused toolkit and is now globally deploying an end-to-end production system digital twin that extends from the well choke to the facility export for system surveillance and optimization. The end-to-end production system digital twin is a cloud-based system that links sensor data from the asset historian with an equipment data model and third-party first principle steady state simulation tools for an accurate representation of the well network and processing facilities. It supports multi-discipline collaboration, particularly between Petroleum Engineers and Process Engineers, and is remotely accessible by a globally dispersed team. This integrated digital twin can be used in two modes: monitoring and what-if. In monitoring mode, the models are automatically updated hourly with real time data and key simulation results extracted and stored. These monitoring simulations generate virtual sensor output, providing insights that cannot be measured by real sensors. In what-if mode, engineers test scenarios risk-free to explore optimization opportunities. As well as routine optimizations to align with production forecast updates, this can also include scenarios during planned abnormal operations (e.g. facility equipment offline for maintenance or well flowback). An early pilot in a key production region delivered significant production upside and was foundational for the subsequent global roll-out program. This paper will illustrate two practical applications from early deployment activities: (1) condensate recovery optimization (2) well routing optimization/feasibility against variable processing facility limits. Β© Copyright 2021, Society of Petroleum Engineers
publications-5347 Article 2020 Conejos Fuertes P.; MartΓ­nez Alzamora F.; HervΓ΅s Carot M.; Alonso Campos J.C. Building and exploiting a Digital Twin for the management of drinking water distribution networks Urban Water Journal 10.1080/1573062X.2020.1771382 Digital Twins (DTs) are starting to be exploited to improve the management of water distribution systems (WDSs) and, in the future, they will be crucial for decision making. In this paper, the authors propose several requirements that a DT of a water distribution system should accomplish. Developing a DT is a challenge, and a continuous process of adjustments and learning is required. Due to the advantages of having a DT of the WDS always available, during the last years a strategy to build and maintain a DT of the water distribution network of Valencia (Spain) and its Metropolitan Area (1.6 million inhabitants) was developed. This is one of the first DTs built of a water utility, being currently in operation. The great benefits of their use in the daily operation of the system ensure that they will begin to be usual in the most advanced smart cities. Β© 2020 Informa UK Limited, trading as Taylor & Francis Group.
publications-5348 Conference paper 2020 Anton S.D.D.; Schotten H.D. Intrusion Detection in Binary Process Data: Introducing the Hamming-distance to Matrix Profiles Proceedings - 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020 10.1109/WoWMoM49955.2020.00065 The digitisation of industry provides a plethora of novel applications that increase flexibility and reduce setup and maintenance time as well as cost. Furthermore, novel use cases are created by the digitisation of industry, commonly known as Industry 4.0 or the Industrial Internet of Things, applications make use of communication and computation technology that is becoming available. This enables novel business use cases, such as the digital twin, customer individual production, and data market places. However, the inter-connectivity such use cases rely on also significantly increases the attack surface of industrial enterprises. Sabotage and espionage are aimed at data, which is becoming the most crucial asset of an enterprise. Since the requirements on security solutions in industrial networks are inherently different from office networks, novel approaches for intrusion detection need to be developed. In this work, process data of a real water treatment process that contains attacks is analysed. Analysis is performed by an extension of Matrix Profiles, a motif discovery algorithm for time series. By extending Matrix Profiles with a Hamming-distance metric, binary and tertiary actuators can be integrated into the analysis in a meaningful fashion. This algorithm requires low training effort while providing accurate results. Furthermore, it can be employed in a real-time fashion. Selected actuators in the data set are analysed to highlight the applicability of the extended Matrix Profiles. Β© 2020 IEEE.
publications-5349 Conference paper 2021 Ye H.; Shang D.; Liang T.; Yan J.; Hua T.; Rui J. Research on integration application of BIM technology in water conservancy and hydropower automation system 2021 3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021 10.1109/IAECST54258.2021.9695854 This paper summarizes the functional characteristics of building information model (BIM) and the current business application status, and puts forward the general architecture and key technologies for the integration of BIM and water conservancy and hydropower automation system in combination with the operational business requirements of water conservancy and hydropower automation system. Furthermore, it describes the integration application scenarios and discusses the problems to be solved in the application of BIM and automation systems, which can provide references for the application of BIM in automation systems in the industry. Β© 2021 IEEE.
publications-5350 Conference paper 2020 Murillo A.; Taormina R.; Tippenhauer N.; Galelli S. Co-Simulating Physical Processes and Network Data for High-Fidelity Cyber-Security Experiments ACM International Conference Proceeding Series 10.1145/3442144.3442147 Recently, Digital Twin-based solutions have been proposed as experimentation platforms to study the behaviour of Cyber-Physical Systems (CPS) under attack, and design appropriate detection and mitigation measures. Existing solutions focus on physical process, control logic, or network communication simulation. Unfortunately, none of the Digital Twin solutions currently available provide arealistic and holistic solution to represent all three aspects. In this work, we propose the Digital HydrAuLic SIMulator (DHALSIM), a Digital Twin for water distribution systems that simulates physical, control, and network processes. DHALSIM builds on the integration of the Water Network Tool for Resilience (WNTR) hydraulic simulator and MiniCPS - an industrial network emulator - which are run in a co-simulation environment. Thanks to this integration, DHALSIM is able to simulate the hydraulic processes characterizing a water distribution system as well as full stack emulation of well-known industrial control protocols. The Digital Twin is demonstrated on the benchmark case study of C-Town, where we carry out a number of cyber-attack experiments. To our knowledge, DHALSIM is the first Digital Twin that implements a well known physics simulator with a virtual industrial logic and network emulation environment for water systems. DHALSIM is open source and available to the research community. Β© 2020 ACM.