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-5301 Conference paper 2022 Adeyemo H.B.; Bahsoon R.; Tino P. Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical Systems Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022 10.1109/BDCAT56447.2022.00028 Cyber Physical Systems (CPS) pose a pressing need to ensure they are sufficiently reliable and continue to be dependable. It is, therefore, essential to test these systems to uncover any potential anomalies, which if not detected can lead to failure and/or cause loss or injury. Adequate or complete coverage of behaviours can be difficult to accomplish in CPS. We advocate a less expensive and easy-to-evaluate representation of the system via surrogate modelling. In this paper, we present a novel predictive fault modelling framework leveraging surrogate-based Digital Twin for probing for likely faults that can support software analysts and testers of CPS in their testing plans. The approach abstracts the CPS and uses a variant of Recurrent Neural Network known as Long Short-Term Memory (LSTM) surrogate model for forecasting. The forecasting can help in predicting multiple behaviours of the system components and the likely faults of systems under test; observations will consequently feed into the testing plans. Both direct and iterative (i.e. one-time and multiple-time varying steps) forecasting are supported as part of the framework. We evaluate our surrogate-based Digital Twins predictive modelling approach on two CPSs namely: water distribution system and air pollution detection system. The results show that our approach performed decently in predicting multiple time steps. Β© 2022 IEEE.
publications-5302 Conference paper 2022 Mihaly N.-B.; Simon-Varhelyi M.; Luca A.-V.; Cristea V.-M. Optimization of the Wastewater Treatment Plant Recycle Flowrates Using Artificial Neural Networks 2022 23rd IEEE International Conference on Automation, Quality and Testing, Robotics - THETA, AQTR 2022 - Proceedings 10.1109/AQTR55203.2022.9801979 Process control in wastewater treatment plants (WWTPs) is, in a varying extent, relying on the operator's experience and is often based only on the quality of the effluent water. An alternative to such not always efficient approach is the development of artificial intelligence tools for plant performance prediction and operation optimization. This work focuses first on developing accurate artificial neural network (ANN) models capable of predicting effluent quality and energy performance indices for the WWTP. Second, these models are further utilized in operation optimization by manipulating the nitrates and activated sludge recirculation flowrates, in association to aeration control. Four categories of ANNs were considered, two of nonlinear autoregressive networks with exogenous inputs (NARX), one of Radial Basis (RBNN) and one of Generalized Regression (GRNN) types. The training dataset was obtained from simulations with an analytical model that was calibrated with real plant data. Topologies of NARX type networks were found to be optimal for predicting performance indices, both for single output and multiple output network structures. The ANN models showed high accuracy, as their mean absolute percentage error (MAPE) values between predicted and targeted outputs were ranging from 0.85% to 3.50%. Optimization using the developed ANN models showed similar results to those obtained with the use of the analytical model. The meaningful difference in the optimization computation time was revealed when comparing it to the optimization performed with the analytical model. The ANNs required four orders of magnitude less computation time, proving the efficacy and potential of the proposed method to be used for real time optimization applications and digital twins implementation. Β© 2022 IEEE.
publications-5303 Conference paper 2022 Hao Z.; Yang L.; Xin L.; Mingkun T. Simulation and testing platform of oil and gas station industrial control system based on digital twin technology Proceedings of SPIE - The International Society for Optical Engineering 10.1117/12.2645728 Aiming at the outstanding problems of long on-site debugging time and high risk of operation and parameter adjustment of industrial control systems in important production and processing bases of oil and gas stations, such as joint stations, transfer stations, and water injection stations, this paper adopts digital twin technology, rapid physical access technology for equipment, PLC and Configuration technology develop the simulation and test platform of the oil and gas station industrial control system, build the mapping relationship between the virtual space simulation model and the measured system entity, and realize the rapid access and reliability verification of the oil and gas station and warehouse PLC industrial control system. The platform significantly improves the operation quality of the industrial control system and reduces the occurrence of production safety accidents. Β© 2022 SPIE.
publications-5304 Conference paper 2022 Ye L.; Zhang L.; Liu C.; Tang J. Study on the Design of Early Warning Release System Based on Digital Management of Early Warning Rules 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum, ICHCE 2022 10.1109/ICHCE57331.2022.10042641 Through the research and design of early warning release, this paper aims to analyze the business scenario of automated early warning in the digital twin basin and the difficulties in its development. Through the analysis, the processing method based on the digitalization of early warning rules is proposed, and the key pages of the automatic early warning release system are designed. In the paper, a solution is provided to abstract the business and transform it into a logical operation, which can be used for reference in building other digital twin basins. Β© 2022 IEEE.
publications-5305 Book chapter 2022 Park Y.; Shin J. BUSAN, SOUTH KOREA Landscape Architecture for Sea Level Rise: Innovative Global Solutions 10.4324/9781003183419-9 Deltas are vulnerable to river and seawater flooding due to their low-lying topography and proximity to bodies of water. In the port city of Busan in Korea, the oceanic climate’s heavy rain increases the risks and concerns of subsequent rainwater flooding. The master plan of Eco-Delta City acknowledges these flood risks and includes a number of mitigation strategies. It adopts different types of structural and non-structural mechanisms, including a ground elevation level increase, which takes into account the 200-year flood plain, a wide riparian buffer zone, and low-impact development strategies. The plan’s Intelligent Flood Monitoring System uses innovative IoT (Internet of Things) and digital twin technologies to provide enhanced flood management. © 2022 selection and editorial matter, Galen D. Newman and Zixu Qiao; individual chapters, the contributors.
publications-5306 Conference paper 2022 Zhou X.; Yang Y.; Xu Y.; Li C.; Ren H. A STUDY ON THE ERROR CHARACTERISTICS AND COMPENSATION ALGORITHM FOR STRAIN GAUGE IN SHIP STRUCTURE MONITORING SYSTEMS Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE 10.1115/OMAE2022-81437 The ship structural stress monitoring system is one of the main technical approaches to realize intelligent ship hull structure, and it has been applied to high-performance ships and large-scale merchant cargo ships in recent years. The structure monitoring system can not only serve as the input of data-driven digital twin models of ships structures, but is also the basis for the derived decision support system. For this reason, it is crucially important to obtain accurate stress in the structure, and the error characteristics of strain gauges under possible combinations of load and temperature that a ship may undergo must be investigated. For a ship structure, the error characteristics of the strain gauges can be investigated by comparing the measures from the strain gauges installed on the ship and the stress obtained from numerical simulation. A neural network to compensate the errors of the strain gauges can be trained through measures from strain gauges and the numerical results for the stress at the same location. In this study, the analysis of the performance and the error characteristics of the strain gauges on a beam test piece are conducted. An experimental investigation of the response of two types of fiber Bragg grating strain gauge at different temperatures and different loads are conducted in the same approach. The performance and error characteristics of the strain gauges under different loads and temperatures are analyzed. Based on the analysis of error characteristics, various BP neural networks are constructed to compensate the errors of the strain gauges. Comparison of compensation results and experimental results of two types of fiber Bragg grating strain gauge shows that the proposed method can effectively reduce the influence of the error on the accuracy of the gauge. Application of this method requires the training samples of measures and numerical results for the stresses under known static loading conditions like berthed in a harbor or moving in calm water. Thus it is feasible to update the neural networks for the compensation of errors during the operation of ships. Β© 2022 by ASME.
publications-5307 Conference paper 2021 Deri E.; Var e C.; Wintergerst M. Development of digital twins of pwr steam generators: Description of two maintenance-oriented use cases International Conference on Nuclear Engineering, Proceedings, ICONE 10.1115/ICONE28-63246 Physical modelling computation, virtual reality, instrumentation and data analysis have shown great progress in the past years. Thus, the integration of all these disciplines in one unique tool seems now achievable. Within this frame, this communication aims at discussing the extension of the Digital Twin concept to the PWR Steam Generators. Operating steam generators communicate some information on their health status during inspection outages, and some other during operation. As, generally speaking, a SG fully satisfies its role of safely produce steam, the goal of an in-service DT is to optimize both the maintenance and the operation, namely: provide the best preparation for next outages, avoid SG-caused unscheduled outages and obtain the best performances. Some general features are outlined with respect to the different roles of Digital Twins both in design and operation. Then the vision of the R&D Division of EDF Company is given for the developed Steam Generators Digital Twin. Two use cases are presented focusing on maintenance. The first one is about chemical cleaning schedule including how the Digital Twin contributes to the prediction of shell side deposit, and the second one is about periodic hydraulic test preparedness, including how the digital twin contributes to the prediction of crack sizes. Β© 2021 American Society of Mechanical Engineers (ASME). All rights reserved.
publications-5308 Article 2021 Callcut M.; Cerceau Agliozzo J.-P.; Varga L.; McMillan L. Digital twins in civil infrastructure systems Sustainability (Switzerland) 10.3390/su132011549 This research explores the existing definitions, concepts and applications surrounding the efficient implementation and use of digital twins (DTs) within civil infrastructure systems (CISs). The CISs within the scope of this research are as follows: transportation, energy, telecommunications, water and waste, as well as Smart Cities, which encompasses all of the previous. The research methodology consists of a review of current literature, a series of semi-structured interviews and a detailed survey. The outcome of this work is a refined definition of DTs within CISs, in addition to a set of recommendations for both future academic research and industry best practice. Β© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publications-5309 Conference paper 2021 Conde López E.R.; Toledo Municio M.Á.; Salete Casino E. Optimization of numerical models through instrumentation data integration: Digital twin models for dams Computational and Mathematical Methods 10.1002/cmm4.1205 Dam safety is a relevant aspect in our society due to the importance of its functions (power generation, water supply, lamination of floods) and due to the potentially catastrophic consequences of a serious breakdown or breakage. Dam safety analyses are fundamentally based on behavior models, which are idealizations of the dam-foundation that allow us to calculate the dam's response to a certain combination of actions. The comparison of this response with the real one, measured by the auscultation or survey devices, is the main element to determine the safety status of the structure. To improve this analysis, it is necessary to increase the accuracy of the numerical models obtaining a digital twin that allows knowing, in a faithful way, how the structure is going to work in normal and extreme situations. © 2021 John Wiley & Sons Ltd.
publications-5310 Conference paper 2021 Yu H.; Hou J.; Xu H. Research on the Digital Twin Model of Fuchunjiang Hydropower Plant Based on 3D Laser Scanning Technology IOP Conference Series: Earth and Environmental Science 10.1088/1755-1315/784/1/012015 With the development and application of 3D laser scanning technology, the efficiency and accuracy of 3D reverse modeling have been greatly improved. In this paper, 3D laser scanning technology is used to establish the 3D model of the hydropower house and main equipments of Fuchunjiang Hydropower Plant, which can truly restore the internal structure and equipments layout of the power station, and solve the difficulties in realizing the digital power station construction due to incomplete or missing drawings of the old power station. The 3D model of the hydropower house and main equipments of Fuchunjiang Hydropower Plant established in this paper not only forms the valuable virtual assets of the power station, but also lays the foundation for flood control emergency plan simulation and 3D maintenance training of the power station, which has a wide field of application with good prospects. Β© Published under licence by IOP Publishing Ltd.