| publications-4921 |
Conference paper |
2024 |
MasΓ³ J.; Brobia A.; Zamzov M.; Serall I.; Hodson T.; Palma R.; Noardo F.; Bastin L.; Lush V. |
Digital Twin Ready Data Available in the Green Deal Data Space |
International Geoscience and Remote Sensing Symposium (IGARSS) |
10.1109/IGARSS53475.2024.10641950 |
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The Destination Earth (DestinE) initiative will develop and deploy a service infrastructure of computer processing, data and software. The Green Deal Data Space will organize the available data to contribute to the data lake of the DestinE infrastructure. This paper focuses on how to overcome data challenges in the Green Deal Data Space by producing Digital Twin Ready Data under the big data paradigm. In the Green Deal Data Space, the traditional organization of data in layers is no longer efficient as data is constantly evolving and mixed in new ways. First, there is a need for a new organization based on a flexible and encompassing Information Model composed by a suite of ontologies reusing best practices, and existing standards. Secondly, dynamic multidimensional data cubes replace the traditional two-dimensional view. Third, the OGC APIs offer a set of building blocks to implement data filtering for extracting the data with its provenance metadata.The AD4GD project will demonstrate the proposed capabilities of the Green Deal Data Space in three pilots about water pollution in Berlin's small lakes, biodiversity connectivity in Catalonia and air quality for the Copernicus Atmosphere Monitoring Service.While data spaces should allow for data exchange in a secure environment that enabling the digital economy, this aspects are out of scope of this communication. Β© 2024 IEEE. |
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| publications-4922 |
Conference paper |
2024 |
Zhang Z.; Liu L.; Zhou W.; Tang B.; Li G.; Liang Z. |
A digital twin modeling method of atmospheric pressure control system for manned space docking |
Proceedings of SPIE - The International Society for Optical Engineering |
10.1117/12.3039586 |
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Establishing a right atmospheric pressure environment is first for astronauts to enter from the visiting vehicle to the target spacecraft in manned space docking and the atmospheric pressure control system plays an integral role in the whole process. However, the judgment of the system's operation state still strongly depends on the experience of ground support technologists. This paper proposes a novel companion flight method for atmospheric pressure control system in manned space docking based on digital twin modeling. The modeling approach contains three main elements as basic theory, modeling tool and model realization, where basic theory provides mathematical description of the system behavior, modeling tool gives modeling support such as modeling language and environment, model realization contains detail modeling and simulation steps. Experiment results showed that the digital twin model calculation data can well follow the real telemetry data. In each stage of the docking atmospheric pressure control process, the error between the calculated output and the real one is less than 5%. The proposed method provides a digitalize way for atmospheric pressure control system in manned space docking and can be extended to various similar systems, when the control objects place in remote areas. Β© 2024 SPIE. |
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| publications-4923 |
Review |
2024 |
Duarte M.S.; Martins G.; Oliveira P.; Fernandes B.; Ferreira E.C.; Alves M.M.; Lopes F.; Pereira M.A.; Novais P. |
A Review of Computational Modeling in Wastewater Treatment Processes |
ACS ES and T Water |
10.1021/acsestwater.3c00117 |
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Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes. Β© 2023 The Authors. Published by American Chemical Society |
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| publications-4924 |
Article |
2024 |
Hofmeister M.; Brownbridge G.; Hillman M.; Mosbach S.; Akroyd J.; Lee K.F.; Kraft M. |
Cross-domain flood risk assessment for smart cities using dynamic knowledge graphs |
Sustainable Cities and Society |
10.1016/j.scs.2023.105113 |
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This paper investigates the usage of knowledge graphs to bridge the gap between current data silos in deriving a holistic perspective on the impact of flooding. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to deploy an ecosystem of autonomous software agents to continuously ingest new real-world information and operate on it. Multiple publicly available yet isolated data sources, including geospatial building information and property sales data as well as real-time river levels, weather observations, and flood warnings, are connected to instantiate a semantically rich ecosystem of knowledge, data, and computational capabilities to provide cross-domain insights in projected flooding events and their potential impact on population and built infrastructure. The extensibility of the proposed approach is highlighted by further integrating power, water, and telecoms infrastructure as part of the very same system, in order to analyse flood-induced asset failures and their propagation across networks. The World Avatar promotes evidence-based decision making during several disaster management phases, supporting both tactical and strategic risk assessments, which supports the United Nations Sustainable Development Goal 11 to improve the assessment of vulnerability, exposure, and risk of communities imposed by flooding events. Β© 2023 |
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| publications-4925 |
Conference paper |
2024 |
Primeau R.; Seidu R.; Li G. |
Towards a Digital Twin for a Drinking Water Source in Norway |
2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024 |
10.1109/ICIEA61579.2024.10665254 |
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Contamination of drinking water supplies by runoffs, wastewater discharge and other anthropogenic sources has potentially dire consequences for human health and the environment. Surface water bodies are especially vulnerable to such contamination. Water quality digital twins which combine hydrodynamic modeling and fusion of data from networked 'internet of things' (IOT) sensors have been proposed as a solution to facilitate more rapid detection and response to contamination. This paper proposes an online dashboard as a primary user interface for a novel water reservoir digital twin architecture using data collected by IOT devices, including unmanned water quality monitoring platform and surface vessels (USVs), The integration of a comprehensive hydrodynamic water quality model with the sensing devices facilitates the validation of a high-resolution model of all water quality parameters. Data is shared between the sensors, model, and an online database allowing for the water reservoir's status and input data to be visualized on an online platform. Emphasis is placed on the system components which communicate the sensing and modeling outputs to users, since development and testing of other components is ongoing. Β© 2024 IEEE. |
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| publications-4926 |
Conference paper |
2024 |
Daniela Huayanca Quispe S.; Mirely Moza Villalobos K.; Rodriguez S. |
Technologies of Industry 4.0 for the next fashion revolution: A systematic literature review |
Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology |
10.18687/LACCEI2024.1.1.582 |
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Fashion industry has undergone significant changes over time due to the variations on perception, exigencies, and activities of customers around the world. Because of the fluctuating and increasing demand, one of the main concerns of fashion-related activities is pollution, particularly water and air pollution. To that, some technologies from industry 4.0 technologies could be useful for mitigating the negative environmental impact in the fashion industry. Based on this challenge, the article aims to identify the most relevant technological applications of Industry 4.0 to enhance sustainability in production and service processes. To achieve this, two methodologies (PICOC and PRISMA) were used to search and select articles for the research. Through inclusion and exclusion criteria, thirty open-access articles from the Scopus database were chosen. Nowadays, it is known that fashion industry activities represent approximately 8% to 10% of global carbon emissions contributes to industrial wastewater pollution (20%), and generates substantial amounts of waste of materials and energy. Hence, there are various Industry 4.0 technologies that can be combined to improve sustainability in different processes within the fashion industry (textile manufacturing, supply chain management, store management, user experience). After the analysis, it was concluded that technologies such as virtual reality, digital twins, and artificial intelligence, belonging to Industry 4.0, offer a potential solution to mitigate the negative environmental impact of the fashion industry an enrich the customer-business interaction. Further works must explore the benefits and limitations of these technologies, focusing in small and midsize enterprises (SMEs). Β© 2024 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved. |
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| publications-4927 |
Conference paper |
2024 |
Pohlmann L.M.; Bhowmik P.K.; Wang C.; Sabharwall P. |
SENSOR ANOMALIES CHARACTERIZATION AND DETECTION VIA MACHINE LEARNING METHODS FOR NUCLEAR POWER PLANTS |
Proceedings of ASME 2024 18th International Conference on Energy Sustainability, ES 2024 |
10.1115/ES2024-131031 |
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Next-generation nuclear reactors pose challenges for effective system monitoring and data management, necessitating the differentiation of anomalous sensor data from noise. While digital twin models hold promise, fundamental analyses using simplified models are needed. This study proposes a foundational approach utilizing simulated data from PCTRAN-generated datasets to emulate reference pressurized-water reactor (PWR) conditions and introduces typical sensor performance and anomalies. The method employs data partitioning, linear regression for preprocessing, and a K-means clustering algorithm for anomaly detection, achieving over 95% precision in identifying anomalies. A parametric study using Monte Carlo Sampling on the anomaly detection algorithm’s input values reveals critical factors such as window size’s impact on accuracy and computational time. Utilizing the Risk Analysis Virtual Environment (RAVEN) tools, a Pareto optimal frontier is determined to balance accuracy and execution time. Sensitivity and uncertainty analysis highlight window size as a critical factor. While further refinement is necessary for practical application, these techniques show promise for enhancing nuclear system monitoring and data management. © 2024 by The United States Government. |
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| publications-4928 |
Conference paper |
2024 |
Adetunji F.O.; Ellis N.; Koskinopoulou M.; Carlucho I.; Petillot Y.R. |
Digital Twins Below the Surface: Enhancing Underwater Teleoperation |
Oceans Conference Record (IEEE) |
10.1109/OCEANS51537.2024.10682270 |
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Subsea exploration, inspection, and intervention operations heavily rely on remotely operated vehicles (ROVs). However, the inherent complexity of the underwater environment presents significant challenges to the operators of these vehicles. This paper delves into the challenges associated with navigation and maneuvering tasks in the teleoperation of ROVs, such as reduced situational awareness and heightened teleoperator workload. To address these challenges, we introduce an under-water Digital Twin (DT) system designed to enhance underwater teleoperation, enable autonomous navigation, support system monitoring, and facilitate system testing through simulation. Our approach involves a dynamic representation of the underwater robot and its environment using desktop virtual reality, as well as the integration of mapping, localization, path planning and simulation capabilities within the DT system. Our research demonstrates the system's adaptability, versatility and feasibility, highlighting significant challenges and, in turn, improving the teleoperators' situational awareness and reducing their workload. Β© 2024 IEEE. |
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| publications-4929 |
Article |
2024 |
Lambert G.; Helbert C.; Lauvernet C. |
Quantization-Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models |
Applied Stochastic Models in Business and Industry |
10.1002/asmb.2899 |
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Numerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel-based methods, such as the Hilbert-Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization-based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models. Β© 2024 The Author(s). Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd. |
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| publications-4930 |
Article |
2024 |
Zheng X.; Shi Z.; Wang Y.; Zhang H.; Tang Z. |
Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction |
Energy |
10.1016/j.energy.2023.129726 |
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As a vital infrastructure in modern cities, district heating (DH) systems provide stable high-quality heat sources. To satisfy the demands of energy-saving, emission-reduction policies, and user preferences, a high-precision digital simulation platform is essential for district heating network (DHN) scheduling. In this paper, a method for digital twin (DT) modeling based on hydraulic resistance identification using an online adaptive particle swarm optimization (APSO) algorithm is proposed. Additionally, Singular Spectrum Analysis and back propagation artificial neural network algorithms (SSA-BP) are proposed to achieve heat load prediction in order to provide sufficient conditions for DHN scheduling. The load prediction for the substation is completed by predicting the secondary return water temperature at the subsequent time step based on the historical outdoor temperature time series and secondary supply water temperature of the substation. Accordingly, simulation analysis has been conducted on its application to an existing DHN in Tianjin. With the proposed methods, the simulation errors are reduced for about 80 % substations in the case DHN compared to mechanistic models when simulating hydraulic conditions. Among these, 77.49 % of substations were reduced by 0 %–3 %, and 1.43 % of substations were reduced by 3 %–5 %. The prediction errors of secondary return water temperature for case substations A and B exhibit random variation trends, with maximum absolute errors within the range of 0.02 °C and 0.06 °C, respectively. © 2023 Elsevier Ltd |
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