| publications-4841 |
Article |
2024 |
Gagliardi G.; Gallelli V.; Violi A.; Lupia M.; Cario G. |
Optimal Placement of Sensors in Traffic Networks Using Global Search Optimization Techniques Oriented towards Traffic Flow Estimation and Pollutant Emission Evaluation |
Sustainability (Switzerland) |
10.3390/su16093530 |
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The relationship between estimating traffic flow and evaluating pollutant emissions lies in understanding how vehicular traffic patterns affect air quality. Traffic flow estimation is a complex field that involves a variety of analytical techniques to understand, predict, and manage the flow of vehicles on road networks. Different types of analyses commonly employed in this area are statistical analysis (e.g., descriptive statistics, inferential statistics, time series analysis), mathematical modeling (macroscopic models, microscopic models, mesoscopic models), computational methods (e.g., simulation modeling, machine learning, and AI techniques), geospatial analysis (e.g., geographic information systems (GISs), spatial data analysis), network analysis (e.g., graph theory and network flow models). In sensor network setups, the strategic placement of sensors is crucial, primarily due to the challenges posed by limited energy supplies, restricted storage capabilities, and the demands on processing and communication, all of which significantly impact maintenance costs and hardware limitations. To mitigate the burden on processing and communication, it is essential to deploy a limited number of sensors strategically. In practical applications, achieving an optimal layout of physical sensors (i.e., placing sensors within the network in such a way as to meet a specific optimality criterion, such as identifying the minimum number of sensors required to ensure the ability to design reliable state observers capable of reconstructing the network’s state based on the available data) is essential for the accurate monitoring of large-scale systems, including traffic flow or the distribution networks of water and gas. In the context of traffic systems, addressing the challenge of full link flow observability, that is, the ability to accurately monitor and assess the flow of entities (i.e., vehicles) across all the links or pathways within a network, entails selecting the smallest number of traffic sensors from a larger set to install. The goal is to choose a subset of p sensors, which may include redundancies, from a pool of (Formula presented.) potential sensors. This is conducted to maintain the structural observability of the entire traffic network. This concept pertains to deducing the complete internal state (traffic volume on each road link in the network) from external outputs and inputs (measurements from sensors). The traditional concept of system observability serves as a criterion for sensor placement. This article presents the development of a simulated annealing heuristic to address the selection problem. The selected sensors are then applied to construct a Luenberger observer, a mathematical construct used in control theory to accurately estimate the internal state of a dynamic system based on its inputs and outputs. Numerical simulations are carried out to demonstrate the effectiveness of this method, and a performance analysis using a digital twin of a transport network, designed using the Aimsun Next software, are also carried out to assess traffic flow and associated pollutant emissions. In particular, we examine a traffic network comprising 21 roads. We address the sensor selection problem by identifying an optimal set of six sensors, which facilitates the design of a Luenberger observer. This observer enables the reconstruction of traffic flow across the network with minimal estimation error. Furthermore, by integrating this observer with data from the Aimsun Next software, we assess the pollutant emissions related to traffic flow. The results indicate a high accuracy in estimating pollutant levels. © 2024 by the authors. |
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| publications-4842 |
Review |
2024 |
Lu S.; Zhang M.; Xu B.; Guo Z. |
Intelligent quality control of gelatinous polysaccharide-based fresh products during cold chain logistics: A review |
Food Bioscience |
10.1016/j.fbio.2024.105081 |
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Gelatinous polysaccharide-based fresh products are influenced by environmental and temperature changes, and maintaining their quality and freshness has always been a challenge. Intelligent management and control of cold chain logistics systems have been extensively used in transporting and storing these goods to overcome the problem. This review introduces common quality deterioration issues, including those encountered during the transportation and storage of these products, such as softening, water loss, and color changes. The application of intelligent detection technologies, including gas detection, intelligent label, and spectral detection is reviewed to achieve real-time monitoring and evaluation of product status. This article also introduces the Internet of Things, wireless sensor networks, and radio frequency identification for product data transmission. It utilizes artificial neural networks and digital twins to build quality models, achieving better management of gelatinous polysaccharide-based fresh products in the cold chain. Moreover, some preservation techniques are used to increase the longevity of these products in storage and reduce losses in the cold chain. These techniques include irradiation, chemical treatment, and coating preservation. This review will, hopefully, encourage additional work that may help reach the goal of having better intelligent quality control of gelatinous polysaccharide-based fresh products during cold chain logistics. Β© 2024 Elsevier Ltd |
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| publications-4843 |
Article |
2024 |
Brooks J.D.; Lewe J.-H.; Duncan S.; Mavris D. |
Certifying a Water-Efficient Building Based on Actual and Simulated Performance during a Pandemic |
Journal of Sustainable Water in the Built Environment |
10.1061/JSWBAY.SWENG-562 |
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This work discusses the certification of a water-efficient building by collecting actual and simulated operation data in response to the building's occupancy disruption from the COVID-19 pandemic. Actual operation data is delivered across a twelve-month certification period for the Kendeda Building for Innovative Sustainable Design, a 3,437.4 m2 (37,000 ft2) academic building on the Georgia Institute of Technology's Atlanta campus. Simulated operation data is delivered for the same twelve-month certification period, to estimate the building's performance under more realistic occupancy conditions. The ability to simulate the building's operation, demonstrated in this work, was needed to substantiate the building's performance, overcome the uncertainty introduced by its disrupted certification period, and grant confidence in its certification. Actual and simulated operation scenario data are gathered by a combination of physical measurements, and physics-based and data-driven models, discussed in this work. The actual and simulated operating scenarios estimate the building to supply and responsibly infiltrate nearly 19 times and 15 times more water than it uses, respectively, over its certification period. Β© 2024 American Society of Civil Engineers. |
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| publications-4844 |
Conference paper |
2024 |
Nagakura K.; Fushimi T.; Tsutsui A.; Ochiai Y. |
Dynamic Acousto-Caustics in Dual-Optimized Holographic Fields |
Proceedings - SIGGRAPH 2024 Emerging Technologies |
10.1145/3641517.3664384 |
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We aims to computationally replicate the dynamic caustics observed in nature, such as the captivating light patterns found at the pool or within flowing rivers. Traditional computer science approaches have only been able to reproduce these light patterns through static fabrication. We introduce "Dynamic Acousto-Caustics"a method that merges acoustofluidics with optics to dynamically manipulate light caustics by controlling the shape of liquid surfaces. Using computer-controlled acoustic fields to deform the surface of a liquid medium, we generate dynamic light behaviors in fluid patterns unachievable with static refractive surfaces. This research not only extends the understanding and application of controlled caustics across various technical and creative domains but also exemplifies the intersections made possible at the convergence of multidisciplinary research. While devices exist to generate waves by vibrating water surfaces, they could not produce continuous caustics at specific locations and timings. Employing ultrasonics to deform the water surface allows us to directly manipulate the surface to create more continuous animated patterns. We shape caustics through acoustic field manipulation and optimize the visual outcome emanating from the geometry of these manipulated objects within a dynamic system. Our approach leverages the Digital Twin methodology as an optimization strategy to fine-tune the interplay between sound and light. This enhances the understanding and application of controlled caustics in numerous technical, demonstrating the crossing points enabled by the convergence of interdisciplinary research streams. Β© 2024 Owner/Author. |
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| publications-4845 |
Review |
2024 |
Hinsby K.; NΓ©grel P.; de Oliveira D.; Barros R.; Venvik G.; Ladenberger A.; Griffioen J.; Piessens K.; Calcagno P.; GΓ¶tzl G.; Broers H.P.; Gourcy L.; van Heteren S.; Hollis J.; Poyiadji E.; Δ_x008c_Γ΅povΓ΅ D.; Tulstrup J. |
Mapping and understanding Earth: Open access to digital geoscience data and knowledge supports societal needs and UN sustainable development goals |
International Journal of Applied Earth Observation and Geoinformation |
10.1016/j.jag.2024.103835 |
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Open access to harmonised digital data describing Earth's surface and subsurface holds immense value for society. This paper highlights the significance of open access to digital geoscience data ranging from the shallow topsoil or seabed to depths of 5 km. Such data play a pivotal role in facilitating endeavours such as renewable geoenergy solutions, resilient urban planning, supply of critical raw materials, assessment and protection of water resources, mitigation of floods and droughts, identification of suitable locations for carbon capture and storage, development of offshore wind farms, disaster risk reduction, and conservation of ecosystems and biodiversity. EuroGeoSurveys, the Geological Surveys of Europe, have worked diligently for over a decade to ensure open access to harmonised digital European geoscience data and knowledge through the European Geological Data Infrastructure (EGDI). EGDI acts as a data and information resource for providing wide-ranging geoscience data and research, as this paper demonstrates through selected research data and information on four vital natural resources: geoenergy, critical raw materials, water, and soils. Importantly, it incorporates near real-time remote and in-situ monitoring data, thus constituting an invaluable up-to-date database that facilitates informed decision-making, policy implementation, sustainable resource management, the green transition, achieving UN Sustainable Development Goals (SDGs), and the envisioned future of digital twins in Earth sciences. EGDI and its thematic map viewer are tailored, continuously enhanced, and developed in collaboration with all relevant researchers and stakeholders. Its primary objective is to address societal needs by providing data for sustainable, secure, and integrated management of surface and subsurface resources, effectively establishing a geological service for Europe. We argue that open access to surface and subsurface geoscience data is crucial for an efficient green transition to a net-zero society, enabling integrated and coherent surface and subsurface spatial planning. Β© 2024 The Authors |
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| publications-4846 |
Article |
2024 |
PawΕ‚owicz J.A.; Knyziak P.; Krentowski J.R.; Mackiewicz M.; Skotnicka-Siepsiak A.; Serrat C. |
Reverse engineering as a non-invasive examining method of the water tower brick structure condition |
Engineering Failure Analysis |
10.1016/j.engfailanal.2024.108280 |
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Reverse engineering is a method of obtaining information about the geometry of an existing object. To obtain such information, among others: 3D laser scanning is used. The result of measurements using this method is a point cloud. The research examined the possibilities of using scanning data to analyze the technical condition of a historical building. Based on the inventoried point cloud, the deflections of beams and ceilings were determined. The course and width of scratches, cracks and defects were determined. It was found that the basic factor increasing the usefulness of the point cloud for various analyzes is its density, which depends on the accuracy of the scan performed. Thanks to a detailed point cloud, a digital three-dimensional model (digital twin) of the existing object was created and analyzed using computer methods. The aim of the presented research was to evaluate the use of reverse engineering to analyze the condition of a historic water tower. Moreover, the real possibilities and advantages of a relatively new measurement method were checked. The thesis was put forward that laser scanning and reverse engineering are effective methods supporting the assessment of the building condition. Based on the available literature and in situ tests, the problem was described and analyzes were carried out to assess the condition of the historic water tower building. Measurements with a 3D laser scanner and a tachymeter were also carried out in the field. Then, the digital model was used to measure the deflection and possible buckling of beams and ceilings on individual floors. The correctness of the formulated method for solving the problem was verified on the basis of tests of sample structural elements. It has been shown that it is possible to directly identify the elevations of the bottom of the ceiling elements and verify whether the existing deflections do not exceed the standard limits. As research has shown, a point cloud resulting from reverse engineering is helpful in assessing the technical condition of a building. Combining classic inspection-based methods with the capabilities of the new measurement method allows for more accurate and broader analyses. Β© 2024 Elsevier Ltd |
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| publications-4847 |
Article |
2024 |
Bai F.; Tang Z.; Yin R.-J.; Quan H.-B.; Chen L.; Dai D.; Tao W.-Q. |
A novel ‘3D + digital twin + 3D’ upscaling strategy for predicting the detailed multi-physics distributions in a commercial-size proton exchange membrane fuel cell stack |
Applied Energy |
10.1016/j.apenergy.2024.124012 |
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With the rapid development of proton exchange membrane fuel cell (PEMFC) commercialization, a comprehensive knowledge of multi-physics fields in large-scale PEMFC stacks has become ever more critical. Although conventional three-dimensional computational fluid dynamic (CFD) models have achieved great success, the application in the commercial-size stack-scale simulation remains inapplicable due to enormous computational resource requirements. Herein, based on the latest 3D CFD model, multi-physics digital twin (DT) technology and 3D stack flow distribution prediction model, a novel multi-scale upscaling prediction model is proposed. The voltage, water and thermal management characteristics of a 164-cell PEMFC stack with an active electrode area of 292.5 cm2 are studied and analyzed in details. For the analysis of commercial-size PEMFC stacks, the most comprehensive multi-physics fields are covered in this paper to date. And the results suggest that by introducing the DT technology, the time requirement of the multi-physics field prediction for unit scale prediction can be reduced by hundreds of thousands of times with a maximum global relative deviation of 1% under 10 groups of random test conditions, giving a solution from the cell scale to stack scale performance prediction, design, heat and thermal management in the PEMFC research and application. Β© 2024 |
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| publications-4848 |
Article |
2024 |
Zohdi T.I. |
A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry |
Computer Methods in Applied Mechanics and Engineering |
10.1016/j.cma.2024.117250 |
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This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management. Β© 2024 Elsevier B.V. |
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| publications-4849 |
Review |
2024 |
Wang A.-J.; Li H.; He Z.; Tao Y.; Wang H.; Yang M.; Savic D.; Daigger G.T.; Ren N. |
Digital Twins for Wastewater Treatment: A Technical Review |
Engineering |
10.1016/j.eng.2024.04.012 |
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The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept, entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants (WWTPs) and sewage networks, hardware (i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software (i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift. Β© 2024 THE AUTHORS |
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| publications-4850 |
Article |
2024 |
Carvalho F.D.C.T.; Nath K.; Serpa A.L.; Karniadakis G.E. |
Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks |
Engineering Applications of Artificial Intelligence |
10.1016/j.engappai.2024.109378 |
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Electrical submersible pumps (ESPs) are prevalently utilized as artificial lift systems in the oil and gas industry. These pumps frequently encounter multiphase flows comprising a complex mixture of hydrocarbons, water, and sediments. Such mixtures lead to the formation of emulsions, characterized by an effective viscosity distinct from that of the individual phases. Traditional multiphase flow meters, employed to assess these conditions, are burdened by high operational costs and susceptibility to degradation. To this end, this study introduces a physics-informed neural network (PINN) model designed to indirectly estimate the fluid properties, dynamic states, and crucial parameters of an ESP system. A comprehensive structural and practical identifiability analysis was performed to delineate the subset of parameters that can be reliably estimated through the use of intake and discharge pressure measurements from the pump. The efficacy of the PINN model was validated by estimating the unknown states and parameters using these pressure measurements as input data. Furthermore, the performance of the PINN model was benchmarked against the particle filter method utilizing both simulated and experimental data across varying water content scenarios. The comparative analysis suggests that the PINN model holds significant potential as a viable alternative to conventional multiphase flow meters, offering a promising avenue for enhancing operational efficiency and reducing costs in ESP applications. Β© 2024 Elsevier Ltd |
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