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 â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5051 Article 2023 Matthews B.; Hall J.; Batty M.; Blainey S.; Cassidy N.; Choudhary R.; Coca D.; Hallett S.; Harou J.J.; James P.; Lomax N.; Oliver P.; Sivakumar A.; Tryfonas T.; Varga L. DAFNI: A computational platform to support infrastructure systems research Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 10.1680/jsmic.22.00007 Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. Β© 2023 Emerald Publishing Limited: All rights reserved.
publications-5052 Conference paper 2023 Prokopovich V.; Podshivalov G.; Martynova L.; Pashkevich I.; Bykova V. Assessment of Motion Safety of a Large Autonomous Underwater Vehicle 2023 International Conference on Ocean Studies, ICOS 2023 - Proceedings 10.1109/ICOS60708.2023.10425633 The problems of ensuring the safe motion of a large autonomous underwater vehicle (AUV) when it passes through sections with different environmental conditions are considered by the example of the development of the Northern Sea Route.. An analysis was made of the influence of various environmental conditions - open and coastal water, fast ice and ice conditions - on the safety of the operation of a large AUV. An approach is proposed to assess the vehicle safety in various environments using a dynamic Bayesian network and conditional probability tables. To test the algorithms for controlling the the vehicle motion and the vehicle itself, a digital test site was developed in the form of a stand for simulating the functioning of the AUV. The development of digital twins in the form of software simulators of the vehicle devices and mechanisms made it possible to test control algorithms on the simulation stand both during normal operation of its systems and in the event of malfunctions due to functioning in various environments. The results obtained on a simulation stand for a large AUV operating on the farthest and most complicated part of the Northern Sea Route - the Vilkitski Strait, helped us to assess the safety of the vehicle operation and outline the ways to improve the safety by upgrading the algorithms for controlling the AUV motion in difficult environmental conditions. Β© 2023 IEEE.
publications-5053 Conference paper 2023 Frepoli C.; Heagy S.A.; Valeri J.; Martin R.P.; Smith C.L.; Vedros K.G. A Demonstration of the Risk-Informed NEI 18-04 Design Evaluation Model for the Modular High Temperature Gas Reactor Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023 10.13182/NURETH20-40251 Several advanced reactor designs are now under active development in the U.S., promising sustainable solutions to the growing world energy needs as well as serving an ever-expanded spectrum of energy applications which are now not limited to electricity production.The designs currently considered are quite diverse and different from the more established light water reactor technology that has dominated the operating commercial nuclear landscape.While advanced reactor concepts were first explored in the dawn of the nuclear age, they are now being reconsidered under the light of modern needs, and specifically, for their flexible operating conditions and inherent safety characteristics.In response, the U.S.Nuclear Regulatory Commission staff is moving forward with development of the 10 CFR Part 53 rulemaking, which is a more risk-informed, technology-agnostic framework for licensing and regulating such new designs.The nuclear industry response to this regulatory initiative resulted in the technical report NEI 18-04 Revision 1, which provides an implementation roadmap of the risk-informed approach when defining the safety case for a new plant design.The implementation of this safety case may be a non-trivial exercise for an actual reactor design.This paper provides a demonstration of performing such analysis for a representative advanced reactor.Public information from the General Atomics High-Temperature Gas Reactor design was considered in this demonstration.The analysis workflow was facilitated with the FPoliSolutions' proprietary Risk-Informed System Engineering (RISE) digital platform, a product that was presented in previous publications.RISE is one application of FPoli's enterprise digital platform, called FPoliAAP, which was created to facilitate orchestration of complex workflows leveraging recent technologies developed at national laboratories such as INL's RAVEN and EMRALD frameworks.The analysis described in the paper includes the selection and classifications of events, the integration of probabilistic risk analysis artifacts, and event modeling simulations for consequence evaluations.The results are then used for SSCs safety classification and, ultimately, a synthesis of the safety case for the design in line with the frequency-consequence targets as presented in NEI 18-04.The purpose of the analysis, as framed in RISE, is to readily produce outputs and views which aids users and regulators in making risk-informed decisions to demonstrate their plant safety case consistent with RG 1.203, RG 1.233 and 10 CFR Part 53. Β© 2023 Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023. All rights reserved.
publications-5054 Article 2023 Edington L.; Dervilis N.; Ben Abdessalem A.; Wagg D. A time-evolving digital twin tool for engineering dynamics applications Mechanical Systems and Signal Processing 10.1016/j.ymssp.2022.109971 This paper describes a time-evolving digital twin and its application to a proof-of-concept engineering dynamics example. In this work, the digital twin is constructed by combining physics-based and data-based models of the physical twin, using a weighting technique. The resulting model combination enables the temporal evolution of the digital twin to be optimised based on the data recorded from the physical twin. This is achieved by creating digital twin output functions that are optimally-weighted combinations of physics- and/or data-based model components that can be updated over time to reflect the behaviour of the physical twin as accurately as possible. The engineering dynamics example is a system consisting of two cascading tanks driven by a pump. The data received by the digital twin is segmented so that the process can be carried out over relatively short time-scales. The weightings are computed based on error and robustness criteria. It is also shown how the error and robustness weights can be used to make a combined weighting. The results show how the time-varying water level in the tanks can be captured with the digital twin output functions, and a comparison is made with three different weighting choice criteria. Β© 2022 Elsevier Ltd
publications-5055 Article 2023 Nasiri G.; Kavousi-Fard A. A Digital Twin-Based System to Manage the Energy Hub and Enhance the Electrical Grid Resiliency Machines 10.3390/machines11030392 This article addresses a digital twin-based real-time analysis (DTRA) to meditate the power system vulnerability whenever cascading failures and blackouts occur for any reason, and thus, to improve the resiliency. In addition to this, a water-power package is proposed to enhance the vulnerable percentage of the system by promptly syringing energy to the grid under line/generator outage contingencies. To this end, in the first place, we will develop a digital twin model along with a cloud platform derived from the Amazon Cloud Service (ACS) into the Amazon Web in order to scrutinize the online vulnerability data arising from the equivalent physical twin in real-time. Indeed, such a DTRA model can help us check the real grid’s behavior and determine how to meet the needs of the energy hub system to prevent blackouts. Additionally, a modified bat-based optimization algorithm is matched to settle the energy between the hub system and the electrical grid in furtherance of real-time analysis. To raise awareness, we will first compile how the hub system interactions can be effective in declining the vulnerability indices, and afterward, we will map out the ACS-based digital twin model on the studied case. © 2023 by the authors.
publications-5056 Article 2023 de Moraes M.G.F.; Lima F.A.R.D.; Lage P.L.D.C.; de Souza M.B., Jr.; Barreto A.G., Jr.; Secchi A.R. Modeling and Predictive Control of Cooling Crystallization of Potassium Sulfate by Dynamic Image Analysis: Exploring Phenomenological and Machine Learning Approaches Industrial and Engineering Chemistry Research 10.1021/acs.iecr.3c00739 Representative mathematical modeling is essential for understanding the batch cooling crystallization processes. Efficient process design and operation are relevant to achieving high-quality criteria and minimizing variation between batches. This work first presents the modeling of batch cooling crystallization based on online dynamic image analysis. A flow-through microscope was used to track the temporal evolution of the crystal population. A population balance modeling (PBM) approach, parameter estimation, and validation were obtained for the batch cooling crystallization of potassium sulfate in water. The performed experiments provided new experimental data, giving dynamic information about the crystal size throughout each run. The kinetic model parameters for crystal nucleation and growth were estimated using a hybrid optimization algorithm, followed by the confidence region construction using a more exploratory particle swarm algorithm. In the parameter estimation framework, in addition to solute concentration, the first fourth-order moments computed throughout all experiments were included in the objective function. A linear size-dependent growth rate was found to capture well the dynamics of the potassium sulfate crystal size distribution. The experimental results evidenced that the crystal shape of potassium sulfate is predominantly constant, allowing the adequacy of the developed model. The validated PBM was also employed as a digital twin of the crystallization process to develop a machine-learning-based control for the process. Then, a surrogate model based on a recurrent neural network, called an echo state network (ESN), was applied in a nonlinear model predictive controller approach (ESN-NMPC). The ESN model could predict the moments of the population balance model up to five steps (5 min) forward. The ESN-NMPC achieved the desired control scenarios for the crystal size and its coefficient of variation. Its performance was comparable to the controller that uses the PBM as the internal model (PB-NMPC). Β© 2023 American Chemical Society
publications-5057 Article 2023 Beckers D.; Eldredge J.D. Wind tunnel effects on gust-interaction simulations Theoretical and Computational Fluid Dynamics 10.1007/s00162-023-00668-9 Abstract: Large-amplitude flow disturbances, or gusts, can drastically alter the aerodynamic forces on an airfoil and are regularly investigated through wind tunnel (or water tunnel) experiments. The gusts generated in those experiments are often further analyzed using numerical simulations, but usually without fully accounting for the wind tunnel walls or gust generator. The current work investigates the wind tunnel effects on the predicted lift response and flow field using a computational framework that models the viscous flow around the airfoil but treats the tunnel walls and gust generation as inviscid boundary conditions. We apply this model to three examples and compare the predicted gust response with the responses predicted by a free-space viscous model and a classical unsteady aerodynamics model to highlight the wind tunnel effects. We find that the wind tunnel modeling introduces non-negligible effects depending on the airfoil and gust configurations. These effects include the confinement effect of the wind tunnel walls and the triggering of flow separation when it does not occur in the corresponding free-space model. In the last example, we also note that this virtual counterpart of an actual wind tunnel can be paired with experiments through data assimilation to increase the accuracy of the gust response or perform parameter estimation. Graphical abstract: [Figure not available: see fulltext.]. Β© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
publications-5058 Article 2023 Liu W.; He S.; Mou J.; Xue T.; Chen H.; Xiong W. Digital twins-based process monitoring for wastewater treatment processes Reliability Engineering and System Safety 10.1016/j.ress.2023.109416 Digital twins are a significant way to achieve fault detection of various smart manufacturing, which provide a new paradigm for complex industrial process monitoring. Wastewater treatment processes play a crucial role in water recycling, its failures may cause risks of adverse environmental impacts. This paper studies the digital twins fault detection framework based on the convolutional autoencoder for wastewater treatment processes monitoring. The designed digital twins fault detection framework can simulate the sludge bulking failure and the toxic impact failure conditions in the virtual space to construct the simulation data with continuous updating through wastewater data. The simulation data is divided into rate of change information sub-block, original sub-block, and cumulative information sub-block using the multi-block modeling strategy to fully explore the hidden information. Further, the sliding window method is utilized to resample the reconstructed sub-blocks to enhance the effects of the detection performance. Bayesian fusion is adopted, and the final decision is made based on the fused statistical value and the control limit. The comparison experiments tested on the digital twins fault detection framework demonstrate the superiority and feasibility of detection performance. Β© 2023 Elsevier Ltd
publications-5059 Conference paper 2023 Jensen J.S. Digital transition in asset management of bridges – Advantages and challenges Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 10.1201/9781003323020-7 Operation and Maintenance (O&M) of bridges are undergoing a digital transformation, and with better technical and more cost-beneficial equipment (drones, sensors, software, etc.), the digital transition accommodates environmental, economic, and technical benefits. Today, most inspections of civil structures are done on-site challenged by traffic restrictions, difficult access- and working conditions, and high initial costs (lift, rope access, etc.). With high-quality RGB - and thermal imagery captured by drones etc., a Reality Model or a visual Digital Twin of the structure may be created, and a Virtual Inspection can be carried out revisiting the structure when needed. The thermal imagery is used in combination with the RGB photo to detect hidden defects in the structure such as delaminations, honeycombing and water ingress. In asset management, the key interest is to find the optimal point in time to repair or replace structural elements considering the Key Performance Indicators (KPIs) that are important to the asset manager or operator. Combining the Reality Model with Artificial Intelligence (AI) for damage detection and monitoring data from implemented sensors on a web-based platform might be the solution to find the optimal point in time for repair, increase the level of safety (for bridge users and O&M personnel), and minimize traffic disturbance. Working with a Reality Model may also decrease the level of subjectivity and increase the level of transparency in decision-making as well as provide efficient means of communication. Despite the many advantages, some challenges remain. They are related to e.g., the reality capture process, AI training, data processing, -presentation, and -integration (also with data-enriched 3D BIM models for recent bridges) in current asset management systems. © 2023 The Author(s).
publications-5060 Article 2023 Laucelli D.B.; Enriquez L.V.; Ariza A.D.; Ciliberti F.G.; Berardi L.; Giustolisi O. A digital water strategy based on the digital water service concept to support asset management in a real system Journal of Hydroinformatics 10.2166/hydro.2023.313 Digital transformation currently represents a clear opportunity for innovation in better management and planning of water distribution networks. Coupled with the increased investments in assets and the growing need of safe and high-quality water for the public, new opportunities are opening for digital tools that can help operators, consultant companies and researchers to support asset management tasks. This work presents the applications of a comprehensive digital water strategy on two real case studies with information provided by the Italian water company Acquedotto Pugliese. The proposed digital water strategy is based on the paradigm named digital water services. The strategy starts by improving the value of GIS and existing models’ data. Then, advanced hydraulic modelling and topological analyses, using the complex network theory, along with artificial intelligence methodologies are the basis for the development of digital water services, which are the engineering apps that use the network digital twin to support the different stages of asset management, i.e., digital water strategy. © 2023 The Authors.