| publications-4861 |
Conference paper |
2024 |
Chan J.Y.; Nor N.S.M.; Huri A.F.A.D.; Sakagami N.; Abidin M.I.Z.; Zawawi F.M.; Ismail K.; Yong J.J. |
Design and Development of Aqua Sense Robot for In-Situ Automatic Water Quality Monitoring |
2024 10th International Conference on Control, Automation and Robotics, ICCAR 2024 |
10.1109/ICCAR61844.2024.10569649 |
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In this paper, the prototype development of Aqua Sense Robot is described. The mechatronic design includes a water sample collecting device allowing for water sampling from various depths hence aiming to monitor water quality on site. The basic functions of the prototype were investigated which involved obstacle avoidance, locomotion and positioning coordinates on the water surface. Furthermore, we present the system design of intelligence water quality index classification as well as digital twin architecture as the virtual representation of Aqua Sense Robot. Β© 2024 IEEE. |
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| publications-4862 |
Conference paper |
2024 |
Kanigolla L.; Pal G.; Vaidhyanathan K.; Gangadharan D.; Vattem A. |
Architecting Digital Twin for Smart City Systems: A Case Study |
Proceedings - IEEE 21st International Conference on Software Architecture Companion, ICSA-C 2024 |
10.1109/ICSA-C63560.2024.00061 |
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Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident's quality of life. However, implementing smart city initiatives comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin (DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This concept offers a virtual replica of assets, processes, and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of smart city systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights into the system's behavior, facilitating informed decision-making. This visualization, coupled with actuation capabilities, enables control actions based on predictive analytics and optimization algorithms, allowing for proactive management of water resources and infrastructure. To this end, in this paper, we present the architecture of WaterTwin, a DT developed for water quality networks in smart city systems. We demonstrate our approach through the use of a water quality network at the smart city living lab, IIIT Hyderabad campus. Β© 2024 IEEE. |
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| publications-4863 |
Book chapter |
2024 |
Parejo A.; Personal E.; Guerrero J.I.; LeΓ³n C. |
Development of an AI-Based Digital Twin Model for Wastewater Treatment Plant |
Springer Proceedings in Materials |
10.1007/978-3-031-64106-0_62 |
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The process of digitalization has become a must in recent years. This digitalization affects not only to data management and communication, but also to most of the processes of the industry. One of the paradigms that have recently gained most attention is the creation of digital twins, i.e., accurate models of processes or elements that can be used to simulate their expected behavior under several possible conditions and scenarios. This modeling makes possible to reach a higher level of optimization thank to the enriched information that it provides. In the case of wastewater treatment plants, the creation of models of each part of the process could be used to identify disparities between the real values measured in the plant and those expected values provided by the models. These disparities are usually related with problems or degradation in the plant elements, unexpected events, or other contingencies, so the study of their values allows to implement predictive maintenance strategies. As part of the β€_x009c_GEDIAV-H2Oβ€_x009d_ project, several monitored variables from a wastewater treatment plant were modeled using artificial intelligence techniques. It can be observed in the case study that the models can effectively predict the expected behavior of the processes in the plant. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
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| publications-4864 |
Conference paper |
2024 |
Degao H.; Jiyu Q.; Tao W. |
Digital twins design for water cold plate using reduced order model |
Journal of Physics: Conference Series |
10.1088/1742-6596/2825/1/012009 |
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Water cold plates are widely used for heat dissipation in electronic devices. The design of water cold plats includes fluid characterization, heat transfer analysis, and fluid-solid coupling analysis, which are mostly carried out by fluid or thermal design software simulation, resulting in low simulation efficiency. This paper proposes a water cold plate simulation method based on digital twins, which realizes the downgrading of the traditional 3D CFD simulation model to a 1D mathematical model (ROM) and greatly shortens the thermal simulation calculation time. Meanwhile, real-time mapping is established between the physical form of the cold plate and the digital twin model to visualize the traditional invisible and unmeasurable parameters. In addition, based on the external real-time measurement data, the digital twin ROM model is driven to perform calculations and output the results, thus realizing the thermal performance evaluation of the cold plate under various working conditions, which greatly improves the efficiency of thermal design and thermal simulation of electronic devices. Β© Published under licence by IOP Publishing Ltd. |
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| publications-4865 |
Conference paper |
2024 |
Fossum K.L.; Bhowmik P.K.; Sabharwall P. |
Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing |
Proceedings of the 2024 International Congress on Advances in Nuclear Power Plants, ICAPP 2024 |
10.13182/T130-44153 |
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To license new and advanced reactor designs, regulators must be convinced that their unique safety casesβ€”relative to existing large-scale reactorsβ€”have been adequately addressed by the designed reactor protection systems. In water-cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval. Β© ICAPP 2024.All rights reserved. |
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| publications-4866 |
Review |
2024 |
Calantropio A.; Chiabrando F. |
Underwater Cultural Heritage Documentation Using Photogrammetry |
Journal of Marine Science and Engineering |
10.3390/jmse12030413 |
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Underwater cultural heritage (UCH) is an irreplaceable resource with intrinsic value that requires preservation, documentation, and safeguarding. Documentation is fundamental to increasing UCH resilience, providing a basis for monitoring, conservation, and management. Advanced UCH documentation and virtualization technologies are increasingly important for dissemination and visualization purposes, domain expert study, replica reproduction, degradation monitoring, and all other outcomes after a metric survey of cultural heritage (CH). Among the different metric documentation techniques, underwater photogrammetry is the most widely used for UCH documentation. It is a non-destructive and relatively inexpensive method that can produce high-resolution 3D models and 2D orthomosaics of underwater sites and artifacts. However, underwater photogrammetry is challenged by the different optical properties of water, light penetration, visibility and suspension, radiometric issues, and environmental drawbacks that make underwater documentation difficult. This paper introduces some of the recent applications of photogrammetric techniques and methods for UCH documentation, as well as the needs and shortcomings of the current state of the art. Β© 2024 by the authors. |
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| publications-4867 |
Conference paper |
2024 |
Song S.; Xiao H.; Jiang L.; Liang Y. |
NEURAL NETWORK BASED DIGITAL TWIN FOR PERFORMANCE PREDICTION OF WATER BRAKE DYNAMOMETER |
Proceedings of the ASME Turbo Expo |
10.1115/GT2024-126589 |
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Water brake dynamometer, the core component of aerospace engine testing facilities, is widely used in turbine component testing and turboshaft engine testing. Since the status of the water brake dynamometer system cannot be monitored in detail, equipment maintenance is performed solely based on the operator's experience, resulting in high risks in the dynamometer equipment operation. Post-test inspections may even reveal damage to the dynamometer bearings and key components. Cavitation and other phenomena require urgent technical solutions to improve health monitoring of key equipment and experimental safety. This paper proposes a performance prediction method for water brake dynamometers based on machine learning. By conducting physical correlation analysis of key parameters, the characteristics of water brake dynamometer operation are captured. Subsequently, a performance prediction model for water brake dynamometer is built based on digital twin technology and experimental data, enabling an accurate mapping of the dynamometer's operational state. After turbine test, the digital model is verified by dynamometer operation data set. Predicted operating parameters of the digital model show that the dynamic mean error between predicted values and actual values of multiple core component temperatures is less than 1%. Considering the sensitivity of data changes, these prediction error values are acceptable, which provide valuable reference information. Β© 2024 by ASME. |
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| publications-4868 |
Article |
2024 |
Alex J. |
Model-Based Construction of Wastewater Treatment Plant Influent Data for Simulation Studies |
Water (Switzerland) |
10.3390/w16040564 |
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The quality of simulations for wastewater treatment plants is heavily dependent on the quality of the simulation input data. Inflow data from wastewater treatment plants collected by measurement cannot usually be used directly for a wastewater treatment plant simulation. A method is presented with which dynamic inflow descriptions for simulation studies can be generated from typical operational measurements. These are volume-proportional 24 h composite samples and continuously recorded inflow water flow rates. To derive the method, a deterministic model was first developed to describe typical dry weather daily inflow concentration patterns and validated for a larger number of measured daily inflow measurements (2 h composite samples). In the second part of the article, the method is then developed with which the dynamic wastewater treatment plant inflow can be calculated for a longer period of time from the modelled dry weather daily inflow and a high-resolution time series of measured flow rates. This dynamic inflow can be used to validate wastewater treatment plant models if additional online measurements for effluent concentrations (e.g., NH4-N and NO3-N) are available. The proposed method is highly suitable for calculating an online estimate of the influent concentrations, which can be used as input information for digital twins, such as observer models and predictive controllers, based solely on the online measurement of the influent flow rate. Β© 2024 by the author. |
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| publications-4869 |
Article |
2024 |
Chen C.; Liu M.; Li M.; Wang Y.; Wang C.; Yan J. |
Digital twin modeling and operation optimization of the steam turbine system of thermal power plants |
Energy |
10.1016/j.energy.2023.129969 |
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The increasing deployment of renewable energy sources necessitates peak regulation services from thermal power plants, impacting their energy efficiency. Central to these plants, the steam turbine system significantly influences their operational efficiency. A digital twin model of this system was developed, integrating mechanism-driven and data-driven modeling methods. The neural network data-driven approach was specifically utilized for parameters such as feedwater pump speed and steam flow rate to the pump turbine. Other parameters were modeled with mechanism data hybrid driven modeling method. This model computes vital metrics such as low-pressure turbine exhaust steam enthalpy, work done and heat absorption per unit mass of steam, system efficiency, feedwater mass flow rate, and water-coal ratioβ€”key for evaluating and enhancing the system's energy efficiency. An investigation into a reference case showed a decline in efficiency below design levels due to aging. By optimizing the live steam pressure and the cold-end system, relative improvements in energy efficiency of 0.35 % and 0.14 %, respectively, were achievable. Β© 2023 Elsevier Ltd |
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| publications-4870 |
Conference paper |
2024 |
Wang X.; Zhao Z.; Li Y.; Jiang C.; Li L.; Zhou Y. |
Research of Key Technologies on Design and Implementation of Intelligent Pump Station |
Journal of Physics: Conference Series |
10.1088/1742-6596/2752/1/012228 |
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With the continuous advancement of technology, intelligent solutions have found widespread applications in various fields, including pump stations in water management projects. This paper focuses on the key technologies involved in intelligent pump station monitoring, control, and operation. The paper takes a pump Station as an example and addresses the problems and current situation in its management. It designs the architecture of an intelligent pump station at three layers: business application, technical methods, and requirements. Solutions are proposed for pump station dispatch and operation, safety management, and engineering maintenance. Finally, by establishing a smart monitoring system based on the five senses and one brain concept and utilizing a dual-factor intelligent verification method in conjunction with digital twin technology. Finally, intelligent management of the pump station is achieved. By exploring these technologies, the paper aims to provide insights into the crucial aspects of designing and implementing intelligent pump stations, thereby facilitating more efficient and intelligent management, control, and operation of water management projects. Β© 2024 Institute of Physics Publishing. All rights reserved. |
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