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-4761	article	2024	Fonseca, P Casas I and Garcia, J Subirana I and Corominas, L and Bosch, LM	Applying a Digital Twin and wastewater analysis for robust validation of COVID-19 pandemic forecasts: insights from Catalonia.	Journal of water and health	10.2166/wh.2024.345	Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.Β© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).
publications-4762	article	2022	Wang, Yajian and Wang, Yajian and Li, Pengpeng and Li, PengPeng and Li, Jianfeng and Li, Jianfeng	The monitoring approaches and non-destructive testing technologies for sewer pipelines	Water Science and Technology	10.2166/wst.2022.120	Abstract Sewage pipe deterioration has been one of the factors causing huge asset losses and urban hazards worldwide. For more efficient and reliable management, a variety of monitoring and non-destructive testing techniques have been developed for defect inspection and condition assessment of sewer pipes. In the present paper, the monitoring approaches of sewage pipes in the form of operational monitoring, structural monitoring, and durability monitoring are outlined. The fundamentals and features of various NDT techniques for different target detect locations are presented. The stereo vision, LIDAR, and laser 3D scanning technologies that might serve to architect the digital twin of the pipeline were also described. What's more, the capabilities and limitations of these technologies are discussed and summarized in tables. Some possible visions for the development of inspection and quantitative evaluation of sewage pipes are also discussed. In practice, it is suggested that visual inspection techniques are the most feasible for the evaluation of underground pipes. In terms of quantitative and automated evaluation, visual inspection robots equipped with stereo vision or laser 3D scanning technology are the most promising.
publications-4763	article	2023	Li, Jiu-Ling and Mohamad, Nur Nabilah Naina and Sharma, Keshab and Yuan, Zhiguo	Establishing boundary conditions in sewer pipe/soil heat transfer modelling using physics-informed learning	Water research	10.1016/j.watres.2023.120441	Modelling heat transfer in sewers and the surrounding soil is important for effective sewer maintenance, and for heat recovery from wastewater. The boundary conditions, including both the thickness of the soil layer to be modelled and the temperature distribution around the boundary of the soil layer, directly determine both the efficiency and accuracy of the models. Yet there is no systematic method to establish these conditions. This study presents a novel and generic approach to establishing efficient boundary conditions for sewer heat transfer modelling. Fourier transform is applied to identify the dominant frequencies of the temperatures of the heat sources/sinks, namely the atmosphere, sewer air and wastewater. A simple data-driven model for determining the thickness of the soil-layer to be included, and three physics-informed models for predicting the temperatures at the soil-layer boundary are then learnt from mechanistic models for sewer heat transfer, taking into consideration the frequency spectra. The methodology achieved high fidelity to the mechanistic models in predicting the soil-layer boundary temperatures and sewer wall temperatures for real-life sewers. This approach offers an easy yet reliable way to obtain efficient boundary conditions that significantly improve both the accuracy and speed of sewer heat transfer modelling.
publications-4764	Article	2024	Vasilijevic A.; BrΓ¶nner U.; Dunn M.; GarcΓa-Valle G.; Fabrini J.; Stevenson-Jones R.; Bye B.L.; Mayer I.; Berre A.; Ludvigsen M.; Nepstad R.	A Digital Twin of the Trondheim Fjord for Environmental Monitoringβ€”A Pilot Case	Journal of Marine Science and Engineering	10.3390/jmse12091530	Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support β€™what-ifβ€™ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. Β© 2024 by the authors.
publications-4765	Article	2024	Popan I.A.; Cosma C.; Popan A.I.; BocΔƒneΘ› V.I.; BΓΆlc N.	Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting	Applied Sciences (Switzerland)	10.3390/app14114901	This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process. Β© 2024 by the authors.
publications-4766	Article	2024	Mengi E.; Becker C.J.; Sedky M.; Yu S.-Y.; Zohdi T.I.	A digital-twin and rapid optimization framework for optical design of indoor farming systems	Computational Mechanics	10.1007/s00466-023-02421-9	In the face of a changing climate and a rising number of β€_x009c_food deserts" in both rural and urban areas, there is a demand to supply fresh produce year-round to communities at the end of the traditional agriculture supply chain. Vertical indoor farming is a promising mode of next-generation agriculture that boasts reduced water and pesticide usage, improved yields, more consistent quality, year-round cultivation, and cheaper transportation and harvesting costs. Indoor farms can rival industrial greenhouses in size, but small-scale β€_x009c_pod farms" can be deployed to smaller communities and areas where large swaths of land are either unavailable or too costly. These pods are often the size of shipping containers with their temperature, humidity, and plant nutrient supply carefully controlled. Plants inside the pods are grown hydroponically with light supplied by panels of LEDs and, thus, this mode of farming is fundamentally different from greenhouse farming. Many indoor farming pods have recently become commercially available claiming high energy efficiency, but little analysis and optimization work has been done to prove these claims. To drive innovation in the design of these physical systems, we have developed a digital-twin and genomic optimization framework for the optical design of vertical indoor farming pods. We model a completely enclosed indoor farming pod with plants in the three mutually-orthogonal planes and illuminated by LED β€_x009c_walls." We employ ray-tracing methods and a genetic algorithm to determine the LED source tube area size, beam aperture spread, and power requirements for maximal power absorption by the plants. Β© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
publications-4767	Article	2024	PatanΓ¨ L.; Iuppa C.; Faraci C.; Xibilia M.G.	A deep hybrid network for significant wave height estimation	Ocean Modelling	10.1016/j.ocemod.2024.102363	The influence of weather conditions on sea state, and in particular on the dynamic evolution of waves, is an important issue that affects several areas, including maritime traffic and the planning of coastal works. To collect relevant data, buoys are used to set up distributed sensor networks along coastal areas. However, unfavourable weather conditions can lead to downtime, which can be extended due to maintenance issues. The ability to improve the robustness of these sensor systems using predictive models, i.e. digital twins, to interpolate and extrapolate missing data is an important and growing area of research. To accomplish such a task, models must be found that can account for both the spatial and temporal dynamics of the input data to correctly estimate the variables of interest. In this work, a deep learning architecture is proposed to realize a digital twin for the monitoring buoy for significant wave height estimation using spatial and temporal information about the wind field in the area of interest. The proposed methodology was applied to a case study using wave height data from an Italian Sea Monitoring Network buoy installed near the coast of Sicily and wind field data from the Copernicus Climate Change Service ERA5 reanalysis. The reported results show that the use of a multi-block hybrid deep neural network consisting of convolutional layers for spatial feature extraction and short-term memory layers for modelling the involved dynamics, which takes into account the buoy surrounding area, outperforms other empirical, numerical, machine learning and deep learning methods used in the literature. Β© 2024 The Authors
publications-4768	Article	2024	Liu F.	Technological Advancements and Prospects of Drill and Blast Tunnel Construction Equipment; [ι’»η†ζ³•ι_x009a_§ι“ζ–½ε·¥θ£…ε¤‡ζ_x008a_€ζ_x009c_―θΏ›ε±•δΈ_x008e_ε±•ζ_x009c_›]	Modern Tunnelling Technology	10.13807/j.cnki.mtt.2024.02.017	With the vigorous development of water conservancy, transportation, energy, and other infrastructure projβƒ ects in China, tunnel and underground engineering construction has entered a period of rapid and significant growth. First, the development of drill and blast construction equipment is introduced, elaborated in four stages: "manual", "small mechanization", "single process large mechanization", and "full process large mechanization". Second, the current status and development trends of problems encountered during drill and blast construction, such as core reβƒ covery rate, overbreak and underbreak control, arch erection efficiency, rebound in wet spraying, and anchor grouting density, are expounded and discussed. The paper also proposes the future key development fields in construction technologies, including drill and blast construction equipment, collaborative management platforms, and green conβƒ struction equipment. Finally, the paper looks ahead to the prospects of intelligent equipment and intelligent conβƒ struction in drill and blast tunnelling, aiming to promote the development of Chinaβ€²s tunnel engineering towards unβƒ manned, intelligent, informative, environmentally friendly, energy-saving, and sustainable goals. Β© 2024 Editorial By Modern Tunnelling Technology. All rights reserved.
publications-4769	Article	2024	Zhang J.; Wang Q.; Gui S.; Zhou J.; Sun J.	Identification of the Key Issues and Technical Paths for Intelligent Operation of Water Source Heat Pump Energy Stations Applying Digital Twin Technology	Applied Sciences (Switzerland)	10.3390/app14125094	To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to use digital twin technology and other information technologies to resolve conflicts between clean energy development and efficient energy utilization. The primary objective of this study is to identify and analyze issues in traditional energy station operations and management systems. Based on this analysis, specific technical solutions are proposed, including pathways for technological research, methodologies, and content. The results provide a comprehensive theoretical framework for the intelligent transformation of energy station systems and essential technical support for the WSHP energy station project in the Hankou Binjiang International Business District. The findings have significant implications for the widespread adoption of WSHP energy stations and the achievement of national carbon neutrality goals. Β© 2024 by the authors.
publications-4770	Article	2024	Zhou S.; Ye Z.; Stefanelli D.	Experiment-modelling-integration of fundamental physiological processes towards digital twin: light-response of photosynthesis as an example	Acta Horticulturae	10.17660/ActaHortic.2024.1395.18	Horticulture industries worldwide face multi-dimensional challenges (e.g., climate change, soil degradation, increasing production costs and global population, etc.). Industry-oriented research and development innovations in the past decades have significantly helped fruit crop industries overcome these challenges, towards modern orchard production systems with enhanced resilience, productivity, profitability and sustainability. Key examples of these innovation efforts include new planting systems optimizing tree crop light relation, sensor technologies auto-monitoring real-time plant functions, data integration and model stimulation towards digital decision tools to support growers identify yield-related issues at early stages, digital twin of perennial orchard production systems (e.g., apple, mango, grapevine) via integrating biological, physical and digital properties and processes - across leaf, canopy, whole-tree and stand levels - to simulate the consequences of changed environment and/or management practices on production systems. However, partly due to the complexity of bridging cross-disciplinary knowledge advancement, there could be fundamental physiological processes which have been experimentally observed for decades but still not been well reproduced by models. Using the light-response of photosynthesis as an example, this paper reviewed recent experiment-modelling-integration efforts towards the accurate model representation. This paper reviewed the performances of two models - the most widely used non-rectangular hyperbolic model (NH model) and a more recently developed mechanistic and nonasymptotic model (Ye model) which has been gaining increasing attention - in fitting light response of 1) photosynthesis, 2) electron transport rate (and its allocation for ribulose bisphosphate carboxylation and oxygenation), and 3) stomatal conductance and water use efficiency, across light-limited (particularly 0-50 ΞΌmol m-2 s-1), light-saturated and photoinhibitory light intensity levels. The accuracy of Ye model and its consistency of model framework in reproducing these concurrent photosynthetic functions under the changing light environment, make it ready to be adopted by the current and future digital twin efforts on two-dimensional multileader narrow orchard systems. Β© 2024 International Society for Horticultural Science. All rights reserved.