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	Abstract
publications-5131	Conference paper	2023	Zhao P.; Yang Y.; Fan G.; Tian M.	Construction and Application of a Knowledge Graph for Flood Control and Emergency Response Plans	Proceedings of SPIE - The International Society for Optical Engineering	10.1117/12.3011573	The digitization of emergency plans is an important foundation for improving the information level of emergency management in China. In response to the problems of low level of intelligence and reliance on traditional document management methods in most emergency plans in the water conservancy industry, this paper focuses on the technical path and methods of constructing a knowledge graph for flood control and emergency response plans. Based on this, and combined with the construction of the knowledge platform of the digital twin project of the Dateng Gorge Reservoir, digital practices of flood control and emergency response plans for Dateng Gorge Reservoir were carried out. The application results provide intelligent support for emergency decision-makers in the smart water conservancy industry and effectively improve the scientific and accurate decision-making in water conservancy emergencies. Β© 2023 SPIE. All rights reserved.
publications-5132	Conference paper	2023	Sabri S.; Alexandridis K.; Koohikamali M.; Zhang S.; Ozkaya H.E.	Designing a Spatially-explicit Urban Digital Twin Framework for Smart Water Infrastructure and Flood Management	2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence, DTPI 2023	10.1109/DTPI59677.2023.10365478	This paper outlines the requirements and challenges of designing and applying spatially explicit urban digital twin technology for managing critical water infrastructure and flood impacts in Smart Cities. It emphasizes the significance of incorporating accurate and reliable location-based data and technologies using Geographic Information Science (GIScience) methods such as Geosimulation, spatial-visual intelligence, and GeoAI, in smart infrastructure systems. Two case studies, Orange County, California, and Victoria, Australia, exemplify this approach. The paper also discusses technical factors and provides a roadmap for creating spatially-explicit urban digital twins to enhance smart urban water and flood management systems. Β© 2023 IEEE.
publications-5133	Conference paper	2023	Ettehadi R.; Onegova E.; Fevang F.Γ.; Knizhnik A.; Postovalov S.; Brevik J.O.; Thompson C., Jr.; Egorenkova T.; Kaageson-Loe N.	Autonomous Drilling Fluid Management System - Development of Fluid Advisory System and First Lab Trial	Proceedings - SPE Annual Technical Conference and Exhibition	10.2118/215047-MS	This paper details a study to design a Fluid Advisory System which uses various algorithms and methods to calculate an optimal quantity of chemical additives which preserve the desired characteristics of the drilling fluid based on manual and real-time measurements while taking inventory into account. The algorithms and results of the first lab trial are presented for water-based mud. The Fluid Advisory System methodology consists of a data pipeline for loading historical data into a master table, a training pipeline for creating a fluid property model (digital twin) using Machine Learning (ML); an automated predictive analytics tool for model selection; and incorporating the predictive models into a prescriptive model that determines quantity of additives to use to achieve a desired set of drilling fluid properties. Given the initial product composition, a set of desired and current fluid properties, the prescriptive model suggests necessary modifications in additive concentrations. This is achieved by setting and solving an optimization problem. First, the Autonomous Fluid Management algorithms have been validated in small-scale lab experiments. Following these successful experiments, a yard-scale trial was conducted at the Equinor Sandsli Automatic Drilling Fluid Laboratory. The yard trial demonstrated how remote control of drilling fluid treatment equipment and the Fluid Advisory System can be employed. The results of the tests reveal that it is now feasible to remotely regulate drilling fluid mixing and apply the ML-based Fluid Advisory System to maintain desired drilling fluid properties for weight, rheology, API fluid loss, pH, hardness, and chlorides. The Fluid Advisory System was developed to aid in the optimization of the real-time drilling fluids treatment process for usage in an Integrated Operations Level 3 environment (IO level 3) and higher (IO level). This takes a step towards autonomous drilling fluid management that will allow for reduction of personnel needed to monitor and maintain a drilling fluid system; more consistent fluid performance and properties; more efficient rig supply chain; lower NPT; safer operations; lower operational cost; and lower carbon footprint. To our knowledge, this is the first time that a Fluid Advisory System for maintaining multiple properties of a drilling fluid was developed and validated experimentally in a remote-controlled mixing facility. Copyright Β© 2023, Society of Petroleum Engineers.
publications-5134	Conference paper	2023	Rincon J.; Greenlee I.; Hamerski R.; Rayborn J.; Schexnayder J.; Tran E.	Vito Operating Model & Start-Up Ramp Up (SURU)	Proceedings of the Annual Offshore Technology Conference	10.4043/32496-MS	In 2009, the Vito field was discovered in more than 4,000 ft of water approximately 150 miles offshore from New Orleans, Louisiana. The project produces from reservoirs nearly 30,000 feet below sea level. This paper outlines the approach taken to develop the Vito facilities operating model (Operating Model) and prepare for start-up and ramp-up. This paper is part of a Vito Project series at OTC 2023, and the other papers are listed in the references. In 2015 the project faced significant financial hurdles and went through a refresh of the concept design to reduce cost and simplify while maintaining safety as a top priority. This re-design resulted in reduced redundancy and operational flexibility including reducing host personnel on board (POB) capacity to 60 personnel. To prepare to start-up and operate the facility with this simplified design, a proactive approach was adopted across four key areas: Operating Model, maintenance strategy, digital building blocks, and start-up ramp-up (SURU) planning. An Operating Model was developed to enable efficient execution of maintenance and operations activities with the reduced POB. This model leverages multiskilling of onsite personnel and enabling digital technologies. The limited POB necessitated development of a maintenance strategy that is both lean and comprehensive with high utilization of the base crew. With local performance standards as the foundation of the strategy, a Reliability Centered Maintenance (RCM) study was performed to validate and further optimize the defined strategy, which relies heavily on asst specific maintenance tasks and onshore sparing due to minimal redundancy and space on host for spares. Digital Twin technology has been leveraged to create a virtual mirror image of the Vito facility that is a central repository of equipment data and documentation. This enables virtual planning of work which reduces POB needs by providing the ability to perform walkdowns, take measurements and identify access issues without being on site. Augmented reality technology supplements this by streaming the viewpoint of offshore staff directly to onshore teams to perform troubleshooting, diagnose issues, and inspect without requiring physical presence. To manage well start-up and to optimize long term recovery, a start-up ramp-up model was implemented with a focus on proactive actions to optimize start-up. This led to long term lease of onshore caverns to eliminate requirements for temporary unloading equipment during start-up. A phased commissioning and Β© 2023, Offshore Technology Conference.
publications-5135	Book chapter	2023	Sheveleva A.V.; Jordanovski M.A.	Digitalization of the Nuclear Industry for Sustainable Development	Springer Climate	10.1007/978-3-031-45830-9_24	In todayβ€™s world, many experts view nuclear power as an eco-friendly energy source producing the smallest possible amounts of harmful emissions into the environment. However, this is not quite the case: uranium is a non-renewable resource, nuclear power plants (NPPs) produce waste, and the disasters that took place at the Chornobyl NPP in 1986 and the Fukushima NPP in 2011 have caused harm not only to the environment by polluting the air and water resources with nuclear substances but also to human lives and health. This is why the risk of error at NPPs must be reduced to zero. Digitalization, which has a special role in implementing global sustainable development goals specifically, could be helpful in this regard. The research aims to analyze the digitalization process of the nuclear industry as a way to improve the safety, efficiency, and performance of NPPs, which helps monitor and control operations in real-time, enhance engineering, operating, and production processes providing clients with various environmental and social benefits, and facilitate the implementation of sustainable development goals. The research concludes that big data technologies help reduce the risk of error when analyzing important indicators by 25%: installation of sensors at the core units of a nuclear power plant makes it possible to control the condition of equipment and monitor the life cycle of the system. Digital twins can be used as training simulators for operators and as simulation environments for engineering experiments and research. Virtual NPPs will be useful in simulating any mode of operation at power units, from normal operations to complex emergencies, thereby preventing incidents and reducing costs. Virtual reality technologies are indispensable in training engineers and specialists whose qualifications and actions are crucial to the safety of NPPs. VR models offer an opportunity to β€_x009c_visitβ€_x009d_ an NPP, take a look at the equipment and learn to work with turbines, which is conducive to making an informed decision if an emergency occurs in reality. Predictive analytics enable the collation and program analysis of all incoming data to generate predictions regarding equipment operation and possible malfunctions. This information can be used to perform preventive maintenance and anticipate abnormal situations. Predictive analysis helps compile and study large amounts of information to detect and diagnose existing and future defects. The results obtained can be used by NPPs and other industries that only start integrating digital technologies into their activities. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
publications-5136	Conference paper	2023	Glass S.W., III; Spencer M.P.; Prowant M.S.; Sriraman A.; Son J.; Fifield L.S.	The ARENA Test Bed β€“ A Versatile Resource for I&C Development and Validation	Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023	10.13182/NPICHMIT23-41079	The Accelerated and Real-time Experimental Nodal Assessment (ARENA) Test Bed at the Pacific Northwest National Laboratory (PNNL) is a versatile resource for development and validation of instrumentation and control (I&C) technologies. This capability was created to facilitate in-situ testing of nuclear electrical cables in various simulated operational environments. Using cable trays, a control box, and selected test components, low voltage cables can be staged to experience local adverse environments such as elevated temperature and water immersion. Cable condition can be continuously monitored over time to track the effect of local stresses using nondestructive assessment tools. A heads-up display (ARENA TV) plots key data in real-time for users. The ARENA Test Bed has recently been used to evaluate the potential for spread spectrum time domain reflectometry (SSTDR) to monitor thermal aging of a portion of live cable powering a three-phase motor. The arrangement provided the opportunity to directly compare the performance of the novel online SSTDR method with offline results from the more standard frequency domain reflectometry (FDR) method. The ability of SSTDR and FDR to identify the presence of water in immersed shielded and unshielded cables and to detect ground faults was also assessed. A digital twin is being developed to track and predict FDR signals from a thermally aging conceptual cable region to compare with measured signals from the ARENA physical counterpart. The test bed concept addresses an important need in nuclear I&C monitoring tool development. New tools and techniques can be developed in the test bed and validated versus known methods and physical measurements. Digital twins and machine learning engines can be populated with measured data in a controlled environment that would not be readily available in the actual nuclear power plant. Proposed monitoring strategies can be confirmed for effectiveness through objective evaluation. It is anticipated that the PNNL ARENA Test Bed will be a valuable resource in advancing nuclear plant instrumentation. Β© 2023 American Nuclear Society, Incorporated.
publications-5137	Conference paper	2023	Pietrangeli I.; Mazzuto G.; Ciarapica F.E.; Bevilacqua M.	Artificial Neural Networks approach for Digital Twin modelling of an ejector	European Modeling and Simulation Symposium, EMSS	10.46354/i3m.2023.emss.007	Digital Twin (DT) is an underused tool in the Oil & Gas industry. Today, the behaviour of Oil and Gas plants is realised by the non-real-time analysis software. In contrast, the DT is a framework capable of controlling and managing a plant in real-time by exploiting sensors, virtual spaces, and the continuous connection between real and digital parts. In this paper, the DT of an experimental plant is presented; the DT is based on a model for evaluating the behaviour of an ejector. In contrast to research on DT in the literature, the proposed model is derived from the use of three Artificial Neural Networks (ANNs) and obtains the values of water pressure (ANN1), airflow (ANN3) and water flow (ANN2) at the ejector inlet. The three Multi Layers Perceptron networks, trained on a dataset obtained from the plant, represent the ejector behaviour at 97.85%, 97.79% and 97.94%, the score of each ANN. This modelling approach for DT is currently not widely used but, given the results, is a good alternative to the traditional techniques used. Β© 2023 The Authors.
publications-5138	Conference paper	2023	Karimi M.A.; Arsalan M.; Shamim A.	Design and Testing of Al Enabled Non-Radioactive Multiphase Fraction Meter	Society of Petroleum Engineers - ADIPEC, ADIP 2023	10.2118/216758-MS	Multiphase flowmeters (MPFM) are commonly used in O&G industry to monitor & optimize production in real time. However, existing multiphase flow meters are either intrusive, radioactive or don't cover full range without requiring frequent calibration. Gases have much different density than liquids and the mixture density is linearly related with the fluid fractions over the full range but the devices to measure density either possess moving parts or rely on radioactive sources (e.g., gamma rays), which are hazardous. On the other hand, some non-density based MPFM relying on dielectric properties of multiphase mixture can be implemented without moving parts, but the dielectric properties have highly non-linear and non-monotonic response with respect to salinity of the produced water, making it extremely complicated to implement. That is why, existing dielectric based MPFM also use a radioactive source specially to measure gas volume fraction (GVF). This paper presents a novel non-radioactive microwave DMOR technology which solves the complex non-linear problem with the use of a digital twin model and custom designed AI algorithm. This unique approach provides a non-intrusive and non-radioactive solution to reliably measure the multiphase fractions without being dependent on fluid mixing conditions. Β© 2023, Society of Petroleum Engineers.
publications-5139	Conference paper	2023	Rakotonirina A.D.; Gonzalez M.; Sainte-Rose B.	ON THE DIGITAL TWIN OF THE OCEAN CLEANUP SYSTEMS	Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE	10.1115/OMAE2023-103055	The Ocean Cleanup is introducing a Digital Twin (DT) describing the cleanup systems made with netted screens to concentrate marine litters and extract them from our oceans. Our DT aims at: i) avoiding over- or under-designing the system; ii) extrapolating from one system to a fleet of systems; and iii) estimating the costs of our offshore operations. These costs are evaluated as cost per kilogram of extracted plastic which is our Key Performance Indicator (KPI). One of the main contributing parameters to the KPI is the hydrodynamic load of our "U-shape" device that spans up to 630 meters and operates at a Speed Through Water (STW or ustw) up to 1.5 m/s equivalent to a twine Reynolds number of Retβ— = 1600. The DT is built with OrcaFlex (OF) using lines and links, the Naumovβ€™s drag correlation and on a no-wave assumption. Data collected from the Great Pacific Garbage Patch (GPGP) are utilised to increase the accuracy of our DT for estimating the loads and the systemβ€™s dynamic deformation. The DT is built using a three-cycle validation: i) initial guess applying the Naumovβ€™s semi-empirical drag correlation to define OF drag coefficients which is excluding the influence of the local angles of attack (AoAs); ii) calibration of the OF drag coefficients using AquaSim (AS) with its twine-by-twine drag correlation for various AoAs; iii) recalibration of the OF drag coefficients from two-dimensional CFD simulations using Direct Numerical Simulation (DNS) for a twine-by-twine establishment of a drag correlation on a one meter segment plane net to account for shielding effects at AoA < 24β—¦. By doing so, we decrease the discrepancy of our OF model, on large spans, with an error less than 15% compared to the GPGP data. For a narrow span, mostly exhibiting very low AoAs, the first cycle shows a 300% discrepancy whereas at the end of the third cycle it shows a 50% discrepancy. Copyright Β© 2023 by ASME.
publications-5140	Article	2023	Li X.; Liang H.; Chen Y.; Ruan Y.; Wang L.	A collaborative model for predictive maintenance of after-sales equipment based on digital twin	European Journal of Industrial Engineering	10.1504/EJIE.2023.133174	In response to the demands of users for prompting fault diagnosis and maintenance, equipment manufacturers require more advanced maintenance technologies for real-time monitoring, prediction, and remote guidance. Based on digital twin, this paper puts forward a seven-dimensional model of collaborative maintenance and a collaborative model for after sales maintenance service, which enables manufacturers to provide more effective and timely service and support to their customers. Taking a bottled water capping process as an example, it constructs a digital twin-driven model for predicting the remaining effective life of devices, a digital twin service platform with a maintenance knowledge database. Based on the forward variable combining the current state and state duration from hidden semi-Markov chain, and the improved formula for calculating the remaining effective life of equipment state, the feasibility of the proposed seven-dimensional collaborative maintenance model and the collaborative model for after sales maintenance service are verified. Β© 2023 Inderscience Enterprises Ltd.