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-4891	Conference paper	2024	Singh A.; Maheshwari A.; Singh S.	Digital Twin Framework for Leakages Detection in Large-scale Water Distribution Systems: A Case Study of IIT-Jodhpur Campus	IFAC-PapersOnLine	10.1016/j.ifacol.2024.05.048	Sustainable development goals and industry 4.0 push for a holistic plan of action for smart water infrastructure enabling advance digital technologies such as Digital Twins for water networks through an integrated use of machine and physical counterparts. This paper proposes a Digital Twin framework for leakage detection applications in large scale water distribution systems. The framework elucidates digital map generation of the network, hydraulics modelling, calibration and leakage detection model in an integrated manner using python interface. The hydraulic model accounting for spatial and temporal variations of network hydraulics and an optimization formulation for calibration and graph neural networks for leakage identification has been developed. The framework is applied, and results have been demonstrated on a real-life case study of IIT Jodhpur campus water distribution system. Β© 2024 The Authors.
publications-4892	Conference paper	2024	Agarwal P.; Hegadi R.; Pai P.P.; Chatterjee S.; Dixit S.	Digital Innovation in Water Governance: Exploring the Synergy of Digital Twins and Blockchain	2024 International Conference on Smart Applications, Communications and Networking, SmartNets 2024	10.1109/SmartNets61466.2024.10577727	Water supply management is a critical aspect of ensuring sustainable access to clean water resources. However, challenges such as water scarcity, aging infrastructure, and inefficient resource allocation persists. This research explores the potential of utilizing a digital twin framework combined with blockchain for effective water supply management. The proposed framework offers numerous benefits, including optimized water allocation, early detection of leaks and anomalies, predictive maintenance, and efficient decision-making. Incorporation of smart contracts in the blockchain infrastructure enables automated governance mechanisms, ensuring compliance with predefined rules and agreements. By demonstrating the feasibility and advantages of combining digital twin technology with blockchain in water supply management systems, this research contributes to the existing body of knowledge. The findings can inform policymakers, water utility operators, and researchers on the practical implementation and potential impact of this innovative approach in addressing water management challenges and fostering sustainable water resource utilization. Β© 2024 IEEE.
publications-4893	Book chapter	2024	Rohilla T.; Kumar M.	Challenges with Sustainable Green Hydrogen Production: Role of Materials, Design, Multi-scale Modeling, and Up-Scaling	Energy, Environment, and Sustainability	10.1007/978-981-97-1339-4_19	In recent years, green hydrogen has emerged as the prime solution for meeting the challenges of the energy crisis and climate change posed by the overuse of fossil fuels. Green hydrogen is the hydrogen produced from water-splitting reactions driven by renewable and sustainable energy resources such as solar, geothermal, hydro, wind, and biomass resources that do not emit greenhouse gases, such as carbon dioxide and others. There are other different mechanisms and conversion routes for producing green hydrogen. Due to the increasing demand for hydrogen for various applications such as steel, off-grid electricity, ammonia, agriculture, and automobiles, there is a need for large-scale green hydrogen production. This technology enhancement is required to meet the coveted target of USD 1 per kg, H2. There are some established technologies such as alkaline, polymer electrolyte membranes, and solid-oxide electrolyzers for hydrogen production. However, several challenges in their efficient utilization are being addressed through the use of suitable materials and design modifications at the appropriate scale of production. Therefore, there is a requirement for a multiscale modeling framework for the selection of compatible and efficient material, and optimum design parameters keeping in mind the safety aspects and the economic viability of scaling criteria. Modeling and digital-twin development are high-performance tools that enhance and utilize the capability of electrolyzers and the associated balance of plants to develop efficient coupling with renewable resources for more efficient hydrogen production. This chapter focuses on the modeling tools and techniques that have been employed to develop reliable models and digital twins of electrolyzers for understanding optimum operating conditions to produce cost-effective hydrogen with high efficiency of conversion in the optimum pressure range. In addition to this, challenges with materials and design aspects have been discussed with a focus on the development of efficient, low-cost electrocatalysts for anode and cathode, porous transport layers, gas diffusion layers, separators, and electrolyte membranes to achieve high conversion efficiency, low gas crossover, and others. In addition, in this chapter, a discussion of the different cell architectures and modular designs of membrane electrolyzers is presented to achieve better conversion efficiency, low gas dissolution, and higher flow rate of the produced hydrogen with minimum components. Moreover, scaling or sizing of the green hydrogen production systems from cells and stacks upΒ to plants (10 MW to 1 GW) requires thorough techno-economic analysis taking into account the renewable energy capacity of the region and the costs of the equipment. Thus, discussions on the techno-economic analyses and case studies of such region-based and renewable energy resource-specific hydrogen production have been presented. Β© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
publications-4894	Article	2024	Morlot M.; Rigon R.; Formetta G.	Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin	Journal of Hydrology	10.1016/j.jhydrol.2023.130587	Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980β€“2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. Β© 2023
publications-4895	Article	2024	Zhang X.; Chen B.	Generation method of hand-drawn feature sketch virtual terrain based on improved generative adversarial network; [ε_x009f_ΊδΊ_x008e_ζ”ΉθΏ› GAN η_x009a_„ζ‰‹η»η‰ΉεΎθ_x008d_‰ε›Ύθ™_x009a_ζ‹_x009f_ε_x009c_°ε½Άη”_x009f_ζζ–Ήζ³•]	National Remote Sensing Bulletin	10.11834/jrs.20233090	The 3D virtual terrain generation is currently used in geography teaching and setting up relevant virtual scenes according to the teaching content, which can visually display the verification experimental conditions and results and enhance the universality of virtual geography experiments. In the field of digital twin smart city, traffic, and water simulation, constructing virtual terrain according to the real terrain can provide the basic environment for subsequent development and experiments. In military simulation, it can quickly build beyond the real scenes of extreme training environment, which has a key role in improving military training and enhancing combat capabilities. Hand-drawn feature sketches can express 3D virtual terrain under human subjective perception. Therefore, how to use hand-drawn feature sketches to build 3D terrain environment quickly and generate realistic virtual geographic environment is a hot spot and difficult point for the creation and development of geographic metaverse with virtual geographic environment as the core in the future. Although the traditional method of generating 3D virtual terrain provides an important reference for image cross-domain generation from hand-drawn feature sketch to virtual terrain, problems such as insufficient realism of the generated terrain remain. Especially when the terrain feature outline is too sparse, the generated terrain will have duplicate terrain blocks and grid artifacts. On this basis, a hand-drawn feature sketch virtual terrain generation method with improved generative adversarial network is proposed. The model is based on extracted data samples and hand-drawn sketch characteristics, and the input terrain feature information is involved in the sampling of each layer by improving the generator U-Net network, which enhances the control role of terrain features in the invisible space, reduces the possibility of model collapse, increases the random noise input, and improves the realism of the generated terrain, especially the detail when the terrain feature elements in the sketch are sparse. L1 loss (mean absolute value error function) and L2 loss (mean variance error function) are combined to form smooth L1 loss, and then optimized with CGAN loss function to form a new generator loss function to improve the stability and efficiency of model training. The Digital Elevation Model (DEM) data of some areas of the Loess Plateau with high accuracy is selected to produce data. The DEM data with high accuracy are selected and used for model training to compare and evaluate the terrain generation enhancement effect quantitatively before and after model improvement. Finally, the model inference process from hand-drawn feature sketch to virtual terrain is completely constructed. The experimental results show the improved virtual terrain generation model with the Loess Plateau terrain data can represent the hand-drawn feature sketch well, and the generated terrain conforms to the distribution and orientation of the terrain features described in the sketch, especially in the case of sparse sketch, and the generated terrain has high realistic surface details. This model is applied to the real natural landscape display and terrain evolution, and it can meet the userβ€™s needs to obtain the virtual terrain with high realistic feeling after inputting the hand-drawn terrain feature sketch. This improved model proposed in this paper has good prospects for 3D terrain modeling and editing. Β© 2024 Science Press. All rights reserved.
publications-4896	Article	2024	Vhora K.; Janiga G.; Lorenz H.; Seidel-Morgenstern A.; Gutierrez M.F.; Schulze P.	Comparative Study of Droplet Diameter Distribution: Insights from Experimental Imaging and Computational Fluid Dynamics Simulations	Applied Sciences (Switzerland)	10.3390/app14051824	The interfacial area between two phases plays a crucial role in the mass transfer rate of gasβ€“liquid processes such as absorption. In this context, the droplet size distribution within the flow field of a droplet-based absorber significantly affects the surface area, thereby influencing the absorption efficiency. This study focuses on developing a computational fluid dynamics (CFD) model to predict the size and distribution of water droplets free-falling in a transparent square tube. This model serves as a digital twin of our experimental setup, enabling a comparative analysis of experimental and computational results. For the accurate measurement of droplet size and distribution, specialized experimental equipment was developed, and a high-speed camera along with Fiji software was used for the capturing and processing of droplet images. At the point of injection and at two different heights, the sizes and distributions of falling droplets were measured using this setup. The interaction between the liquid water droplets and the gas phase within the square tube was modeled using the Eulerianβ€“Lagrangian (E-L) framework in the STAR-CCM+ software. The E-L multiphase CFD model yielded approximations with errors ranging from 11 to 27% for various average mean diameters, including (Formula presented.), (Formula presented.), (Formula presented.), and (Formula presented.), of the liquid droplets at two distinct heights (200 mm and 400 mm) for both nozzle plates. This comprehensive approach provides valuable insights into the dynamics of droplet-based absorption processes. Β© 2024 by the authors.
publications-4897	Book chapter	2024	Thakur S.	Based on Digital Twin Technology, an Early Warning System and Strategy for Predicting Urban Waterlogging	Simulation Techniques of Digital Twin in Real-Time Applications: Design Modeling and Implementation	10.1002/9781394257003.ch14	By gathering data from the actual environment and simulating it, digital twin (DT) technology portrays the twinning behavior of a tangible thing or process, i.e., the current and future behavior. In a variety of fields and industries, including industrial, automotive, medical, smart cities, etc., it is utilized for predictive analysis. In the age of a growing urban population, it is a challenge not only to maintain water quality but also to develop a system that manages water loggingβ€™related problems like sewage overflows and flash floods. Numerous initiatives have failed to meet the needed performance standards because there is insufficient ongoing monitoring of data in real time and a poor knowledge of selfβ€™propelled systems. To address these issues, engineers are seeking to build a network embedded with data sensors and online models using digital twin technology for realβ€™time monitoring of system dynamics. Operators can use this technology to spot odd sewer network conditions and then dispatch a maintenance crew to conduct repairs before damage is done. It results in reducing the operational cost of the system and preventing sewer overflows and waterlogging to a large extent. The main challenge of DT technology in social adoption is the lack of standardization of definitions and characteristics. Performance digital twin (PDT) methodology can be utilized to monitor the information from physical counterparts and produce actionable data for optimizing product design, generating strategy, and drawing conclusions. Instead of achieving economic efficiency, the methodology has provided quality of life and services to the citizens. The DTβ€™s infrastructure includes various data mining and modeling techniques for prediction and optimization, and sensors, actuators, and IoT can be used for data acquisition methods. Β© 2024 Scrivener Publishing LLC.
publications-4898	Conference paper	2024	Marticorena M.; Mayer R.; Vignolo J.; Vaccaro D.; Peyrano O.G.	Virtual Sensor Implementation in Hydroelectric Turbines: A Digital Twin Approach for Clearance Violation Analysis	Mechanisms and Machine Science	10.1007/978-3-031-49413-0_6	This paper presents a methodology for constructing a digital twin model of a vertical turbine used in hydroelectric power generation. The objective is to implement displacement virtual sensors in areas where physical measurements are impractical or unavailable, such as the clearance between the turbine runner and the discharge ring (water-gap). This is achieved by simulating the displacements and deformations of the shaft and support structure, based on on-line measured vibrations. The method consists in constructing a finite elements model which accurately represents the dynamic behavior of the hydrogeneratorβ€™s main components, including the shaft, runner, generator and support structure. During turbine operation, bearing displacements are measured and the model calculates rotor displacements at specified virtual sensor locations to assess potential violations of clearances between stationary and rotating parts. A case study is presented where the validity of the method is evaluated by comparing virtual sensors results with actual measurements during a generator trip event of a 92Β MW Kaplan turbine, demonstrating excellent agreement. Notably, the digital twin detected excessive radial displacement of the runner during the event, indicating a potential violation of the water-gap. This result was corroborated by on-line physical water-gap measurements and visual inspection during maintenance, which revealed evidence of contact between the runner blades and the draft tube. This paper highlights the practical applicability of utilizing a digital twin for rotor dynamic behavior analysis of hydroelectric turbines, ultimately contributing to performance optimization, improved maintenance strategies, enhancing reliability and safety. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
publications-4899	Conference paper	2024	Bali M.K.; Singh M.	Digital Twin and IoT Integration for Precision Agriculture on AWS (Amazon Web Service)	Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024	10.1109/ICAAIC60222.2024.10575908	This study outlines the transformative impact of integrating Digital Twin and IoT technologies for precision agriculture on the AWS platform. Through analysis of quantitative data from farms implementing these technologies, notable advancements in agricultural practices are evident. Digital Twin and IoT integration on AWS have led to significant improvements in precision agriculture. Results reveal an average increase in crop yield of 15-20% compared to traditional methods, accompanied by substantial water savings of 30-40% per hectare. Cost reductions of 25-30% in operational expenses are observed, attributed to optimized resource management, including a decrease in fertilizer usage by 20-25% and pesticide application by 15-20%. Furthermore, the integration facilitates enhanced decision-making processes, with farms reporting a 30-35% increase in decisionmaking accuracy for crop management, irrigation scheduling, and pest control.. The scalability and flexibility of AWS infrastructure enable farms of varying sizes and locations to adopt these technologies according to their specific needs. Continuous improvement is supported by the analysis of data collected through Digital Twin and IoT technologies, allowing farms to refine their strategies over time. Β© 2024 IEEE.
publications-4900	Conference paper	2024	Liu S.; Sun H.; Miao S.	Visualization of Digital Twin Model of Wastewater Treatment Plant Based on Multi-modal Data	Lecture Notes in Electrical Engineering	10.1007/978-981-97-2447-5_40	In this paper, we discussed in detail the construction of digital twin model applied to sewage treatment plants, to solve the problem of the operating pressure of sewage treatment plants increases year by year with the development of cities. Different from the traditional digital twin system which uses pure Building Information Management (BIM) as visual model, this paper designs a multi-modal visual model based on multi-source data such as Unmanned Aerial Vehicle (UAV) and drawings. The three sub-models of the model can complete different visualization functions respectively, and each sub-model can be applied to Personal Computer (PC) and Mixed Reality (MR) head-mounted devices in different working scenarios such as central control room inspection and plant inspection. The construction of sub-models can also provide new data types for the data-driven process of digital twins, and further assist the construction of other sub-models. This digital twin system integrates sewage plant data to assist staff in process decision making and shorten staff training cycle. The architecture and construction method of our model are described in detail in this paper. Β© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.