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 â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5351 Erratum 2020 Erratum regarding missing Declaration of Competing Interest statements in previously published articles (Virtual Reality & Intelligent Hardware (2019) 1(5) (461–482), (S2096579619300634), (10.1016/j.vrih.2019.09.002)) Virtual Reality and Intelligent Hardware 10.1016/j.vrih.2020.11.003 Declaration of Competing Interest statements were not included in published version of the following articles that appeared in previous issues of Virtual Reality & Intelligent Hardware. Hence, the authors of the below articles were contacted after publication to request a Declaration of Interest statement: 1 β€_x009c_Temporal continuity of visual attention for future gaze prediction in immersive virtual realityβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/2: 142-152) https://doi.org/10.1016/j.vrih.2020.01.002 Declaration of competing interest: The Authors have no interests to declare. 2 β€_x009c_Two-phase real-time rendering method for realistic waterβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/2: 132-141) https://doi.org/10.1016/j.vrih.2019.12.005 Declaration of competing interest: The Authors have no interests to declare. 3 β€_x009c_End-to-End Spatial Transform Face Detection and Recognitionβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2020 Hardware, 2020; 2/2: 119-131) https://doi.org/10.1016/j.vrih.2020.04.002 Declaration of competing interest: The Authors have no interests to declare. 4 β€_x009c_Personalized cardiovascular intervention simulation systemβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/2: 104-118) https://doi.org/10.1016/j.vrih.2020.04.001 Declaration of competing interest: The Authors have no interests to declare. 5 β€_x009c_Laser Scanned Real Environment for Intelligent Virtualization of Crane Liftingβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2020 Hardware, 2020; 2/2: 87-103) https://doi.org/10.1016/j.vrih.2020.04.003 Declaration of competing interest: The Authors have no interests to declare. 6 β€_x009c_Study on the adaptability of augmented reality smartglasses for astigmatism based on holographic waveguide gratingβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/1: 79-85) https://doi.org/10.1016/j.vrih.2019.12.003 Declaration of competing interest: The Authors have no interests to declare. 7 β€_x009c_Study of ghost image suppression in polarized catadioptric virtual reality optical systemsβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/1: 70-78) https://doi.org/10.1016/j.vrih.2019.10.005 Declaration of competing interest: The Authors have no interests to declare. 8 β€_x009c_Survey on path and view planning for UAVsβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/1: 56-69) https://doi.org/10.1016/j.vrih.2019.12.004 Declaration of competing interest: The Authors have no interests to declare. 9 β€_x009c_View Synthesis from multi-view RGB data using multilayered representation and volumetric estimationβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/1: 43-55) https://doi.org/10.1016/j.vrih.2019.12.001 Declaration of competing interest: The Authors have no interests to declare. 10 β€_x009c_Virtual assembly framework for performance analysis of large opticsβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2020 Hardware, 2020; 2/1: 28-42) https://doi.org/10.1016/j.vrih.2020.01.001 Declaration of competing interest: The Authors have no interests to declare. 11 β€_x009c_A smart assistance system for cable assembly by combining wearable augmented reality with portable visual inspectionβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2020 ware, 2020; 2/1: 12-27) https://doi.org/10.1016/j.vrih.2019.12.002 Declaration of competing interest: The Authors have no interests to declare. 12 β€_x009c_Co-axial depth sensor with an extended depth range for AR/VR applicationsβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2020 Hardware, 2020; 2/1: 1-11) https://doi.org/10.1016/j.vrih.2019.10.004 Declaration of competing interest: The Authors have no interests to declare. 13 β€_x009c_Research on the visual elements of augmented reality assembly processesβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2019 Hardware, 2019; 1/6: 622-634) https://doi.org/10.1016/j.vrih.2019.09.006 Declaration of competing interest: The Authors have no interests to declare. 14 β€_x009c_Three-dimensional virtual-real mapping of aircraft automatic spray operation and online simulation monitoringβ€_x009d_ (Virtual Reality & Intelligent Hard ware, 2019 ware, 2019; 1/6: 611-621) https://doi.org/10.1016/j.vrih.2019.10.003 Declaration of competing interest: The Authors have no interests to declare. 15 β€_x009c_Digital assembly technology based on augmented reality and digital twins: a reviewβ€_x009d_ (Virtual Reality & Intelligent Hardware, 2019 Hardware, 2019; 1/6: 597-610) https://doi.org/10.1016/j.vrih.2019.10.002 Declaration of competing interest: The Authors have no interests to declare. Β© 2019 Beijing Zhongke Journal Publishing Co. Ltd
publications-5352 Conference paper 2021 Mohanty S.; Elmer T.W.; Bakhtiari S.; Vilim R.B. A REVIEW OF SQL VS NOSQL DATABASE FOR NUCLEAR REACTOR DIGITAL TWIN APPLICATIONS: WITH EXAMPLE MONGODB BASED NOSQL DATABASE FOR DIGITAL TWIN MODEL OF A PRESSURIZED-WATER-REACTOR STEAM-GENERATOR ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) 10.1115/IMECE2021-73153 In this paper a summary of the differences between structured query language (SQL) based traditional relational database management systems (RDBMS) and recently popular NoSQL based database are presented. The importance of selecting a NoSQL database for the implementation of digital Twin (DT) framework for nuclear reactor predictive maintenance has been discussed. Example of commercially available MongoDB based NoSQL database implementation with storing data from various sources: such as from virtual time-series temperature measurements (based on finite element-based heat transfer models) at thousands of nodal locations and from real sensor measurements (from continuous online monitoring based active ultrasonic sensors and from noncontinuous nondestructive testing (NDT) based eddy current measurements is demonstrated. Copyright Β© 2021 by The United States Government
publications-5353 Article 2021 Baitalow K.; Wypysek D.; Leuthold M.; Weisshaar S.; Lölsberg J.; Wessling M. A mini-module with built-in spacers for high-throughput ultrafiltration Journal of Membrane Science 10.1016/j.memsci.2021.119602 Ultrafiltration membrane modules suffer from a permeate flow decrease arising during filtration and caused by concentration polarization and fouling in, e.g., fermentation broth purification. Such performance losses are frequently mitigated by manipulating the hydrodynamic conditions at the membrane–fluid interface using, e.g., mesh spacers acting as static mixers.This additional element increases manufacturing complexity while improving mass transport in general, yet accepting their known disadvantages such as less transport in dead zones. However, the shape of such spacers is limited to the design of commercially available spacer geometries. Here, we present a methodology to design an industrially relevant mini-module with an optimized built-in 3D spacer structure in a flat-sheet ultrafiltration membrane module to eliminate the spacer as a separate part. Therefore, the built-in structures have been conceptually implemented through an in-silico design in compliance with the specifications for an injection molding process. Ten built-in structures were investigated in a digital twin of the mini-module by 3D-CFD simulations to select two options, which were then compared to the empty feed channel regarding mass transfer. Subsequently, the simulated flux increase was experimentally verified during bovine serum albumin (BSA) filtration. The new built-in sinusoidal corrugation outperforms conventional mesh spacer inlays by up to 30% higher permeation rates. The origin of these improvements is correlated to the flow characteristics inside the mini-module as visualized online and in-situ by low-field and high-field magnetic resonance imaging velocimetry (flow-MRI) during pure water permeation. © 2021 Elsevier B.V.
publications-5354 Article 2021 Hu W.; He Y.; Liu Z.; Tan J.; Yang M.; Chen J. Toward a digital twin: Time series prediction based on a hybrid ensemble empirical mode decomposition and BO-LSTM neural networks Journal of Mechanical Design 10.1115/1.4048414 Precise time series prediction serves as an important role in constructing a digital twin (DT). The various internal and external interferences result in highly nonlinear and stochastic time series. Although artificial neural networks (ANNs) are often used to forecast time series because of their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on an ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency and stationary signal) and a residual signal. The decomposed signals are used to train the neural networks, in which the hyperparameters are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed EEMD-BO-LSTM neural networks, this paper conducts two case studies (the wind speed prediction and the wave height prediction) and implements a comprehensive comparison between the proposed method and other approaches including the persistence model, autoregressive integrated moving average (ARIMA) model, LSTM neural networks, BO-LSTM neural networks, and EEMD-LSTM neural networks. The results show an improved prediction accuracy using the proposed method by multiple accuracy metrics. Copyright Β© 2021 by ASME
publications-5355 Article 2021 Shi J.; Li J.; Usmani A.S.; Zhu Y.; Chen G.; Yang D. Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach Energy 10.1016/j.energy.2020.119572 Computational Fluid Dynamic (CFD) has been widely used for the gas release and dispersion modeling, which however could not support real-time emergency response planning due to its high computation overhead. Surrogate models offer a potential alternative to rigorous computational approaches, however, as the point-estimation alternatives, the existing neural network-based surrogate models are not able to quantify the uncertainty of the released gas spatial concentration. This study aims to fill a gap by proposing an advanced hybrid probabilistic Convolutional-Variational Autoencoder-Variational Bayesian neural network (Conv-VAE-VBnn). Experimental study based on a benchmark simulation dataset was conducted. The results demonstrated the additional uncertainty information estimated by the proposed model contributes to reducing the harmful effect of too ‘confidence’ of the point-estimation models. In addition, the proposed model exhibits competitive accuracy with R2 = 0.94 compared and real-time capacity with inference time less than 1s. Latent size Nz = 2, noise σz=0.1 and Monte Carlo sampling number m = 500 to ensure the model's real-time capacity, were also given. Overall, our proposed model could provide a reliable alternative for constructing a digital twin for emergency management during the exploration and exploitation of marine natural gas hydrate (NHG) in the near future. © 2020 Elsevier Ltd
publications-5356 Book chapter 2021 Tsolakis N.; Bechtsis D.; Vasileiadis G.; Menexes I.; Bochtis D.D. Sustainability in the Digital Farming Era: A Cyber-Physical Analysis Approach for Drone Applications in Agriculture 4.0 Springer Optimization and Its Applications 10.1007/978-3-030-84156-0_2 This research introduces an integrated approach for the ex-ante analysis of technology innovations in agriculture, like unmanned aerial vehicles (UAVs), to foster the envisaged sustainable transformation in farming operations. The strategic foresight process of global institutions recognizes the beneficial role of β€_x009c_digital twinsβ€_x009d_ in agriculture for ensuring natural resources’ stewardship and farmers’ livelihood. However, farmers often encounter ambiguities in comprehending the operationalization of digital technologies in agricultural fields as the related scientific and experimental evidence myopically focus on theΒ technical specifications of the respective machinery. Indicatively, the adoption of UAVs for informing decisions related to precision irrigation is often overlooked, while the pertinent literature is sparse and dispersed. This research facilitates the adoption of UAVs for freshwater stewardship in farming operations by: (i) identifying and summarizing advantages and disadvantages related to the utilization of UAVs (commonly known as drones) in agriculture; and (ii) examining β€_x009c_digital twinsβ€_x009d_ in agriculture by proposing a cyber-physical analysis approach for UAVs supporting precision irrigation activities. Specifically, this research develops an emulation model that helps interrogate a UAV’s operational aspects with regard to monitoring water stress levels in an orchard and then implements equivalent real-world intelligent aerial systems for the autonomous identification of the water status of plants to inform precision irrigation operations in an agricultural field. Β© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publications-5357 Article 2021 Tariq R.; Cetina-QuiΓ±ones A.J.; Cardoso-FernΓ΅ndez V.; Daniela-Abigail H.-L.; Soberanis M.A.E.; Bassam A.; De Lille M.V. Artificial intelligence assisted technoeconomic optimization scenarios of hybrid energy systems for water management of an isolated community Sustainable Energy Technologies and Assessments 10.1016/j.seta.2021.101561 Water is an essential resource demanded worldwide and it is quite debatable owing to the economic, political, and energy characteristics of any region. Off-grid water filtration plants are an alternative for communities where transportation of freshwater becomes a real challenge due to a lack of infrastructure for the water potabilization processes. For such potable water filtration plants, hybrid renewable energy systems (HRES) can be a viable solution to meet their energy demand meanwhile providing a sustainable water solution. The main contribution of this work is the unique methodology, which starts with a sizing procedure of various hybrid energy systems using a commercial software β€_x009c_Hybrid Optimization of Multiple Energy Resources (HOMER)β€_x009d_ and spreadsheet algorithms, followed by a β€_x009c_Non-dominating Sorting Genetic Algorithm II (NSGA-II)β€_x009d_ based multiobjective optimization. Single-objective optimization scenarios contain photovoltaic installation capacity, wind turbines, diesel generators, and battery energy storage systems including Pb-acid (Lead-acid), Li-ion (Lithium-ion), and AGM (Absorbent Glass Mat) technologies as design variables to maximize the cost of electricity or net-present-cost. Multiobjective optimization also involved environmental (CO2 emissions i.e. carbon dioxide emissions) and water cost indices as an additional packet to single-objective optimization scenarios. Afterward, a multicriteria decision-making tool using β€_x009c_The Order of Preference by Similarity to Ideal Solution (TOPSIS)β€_x009d_ is applied on the Pareto front to attain the final optimization results. The analysis is further explored in depth by generating digital twins (surrogate or meta model) of HRES data using artificial intelligence techniques (artificial neural network and group-method-of-data-handling). Furthermore, calculus and statistical sensitivity analysis assist in the identification of the significant variables in the design procedure. In summary, the technical contribution of this work can be divided into two sections. The first one is the design of a hybrid energy system for the water management of an isolated community of the indigenous Mayan region of Yucatan, Mexico, which has never been considered before. Secondly, the technical contribution is related to the usage of environmental emissions as an objective function, which is not considered in the traditional design of hybrid energy systems by the software HOMER. Environmental emission as an objective function is not considered while designing a hybrid energy system in commerical softwares like HOMER, in fact, HOMER provides a list of environmental impacts but it is a secondary outcome as a result of technoeconomic optimization. Analysis of results between HOMER pro and spreadsheet has shown conformity, reporting that the optimal case consists of a photovoltaic system, diesel generator, and Li-ion technology of battery storage with capacities of ∼17 kW, ∼5kW, and 44–48 kWh, respectively, corresponding to a net present cost ranging from 70,000 United States Dollars (USD) to 79,000 USD and a cost of electricity ranging from 0.205 to 0.229 USD/kWh. The achievements obtained with multiobjective optimization indicate that the cost of electricity and net present cost can be further reduced by 0.86 % and 0.73 %, respectively, at a decrement of only 0.4% of the renewable fraction as compared to the single objective optimization scenario. It is concluded that multiobjective optimization provides an add-in feature to HOMER by using environmental emissions as an objective function. The design procedure and adapted methodology can be useful to promote sustainable development in the statewide context and can provide a scientific justification to national energy policymakers. Β© 2021 Elsevier Ltd
publications-5358 Article 2021 Yang H.; Ramirez Lopez P.E.; Vasallo D.M. New Concepts for Prediction of Friction, Taper, and Evaluation of Powder Performance with an Advanced 3D Numerical Model for Continuous Casting of Steel Billets Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science 10.1007/s11663-021-02209-3 Continuous casting of steel in an industrial billet caster is modeled numerically including multiphase turbulent flow, mold electromagnetic stirring (M-EMS), heat transfer, and solidification. Two different steel grades (case-hardening and micro-alloyed steel) and casting powders are considered in the study to evaluate the castability and powder performance. Existing models to estimate thermophysical properties of casting powders are reviewed and compared to measurement data. Complex mold taper design is considered by constructing a digital twin and applying a corresponding velocity for the solidified shell in 3D. Slag infiltration is simulated from the beginning of casting to steady operation as a function of shell solidification and resulting heat transfer between liquid steel and oscillating mold wall. Additionally, the model predicts air gap size, excessive taper, and mold friction through a quasi-thermomechanical analysis. This includes a new approach to estimate mold friction based on Lubrication Index (LI) and Contact Index (CI) concepts. The resulting shell thickness, cooling water temperature, nail-dipping measurement, and mold friction are compared to plant data and literature for validation. This novel modeling approach can address phenomena difficult to analyze on real casters such as slag entrainment and infiltration, corresponding thermal response, and contact conditions between shell, slag, and mold. Β© 2021, The Minerals, Metals & Materials Society and ASM International.
publications-5359 Article 2021 Zhang T.; Sun S. An exploratory multi-scale framework to reservoir digital twin Advances in Geo-Energy Research 10.46690/ager.2021.03.02 In order to make full use of the information provided in the physical reservoirs, including the production history and environmental conditions, the whole life cycle of reservoir discovery and recovery should be considered when mapping in the virtual space. A new concept of reservoir digital twin and the exploratory multi-scale framework is proposed in this paper, covering a wide range of engineering processes related with the reservoirs, including the drainage, sorption and phase change in the reservoirs, as well as extended processes like injection, transportation and on-field processing. The mathematical tool package for constructing the numerical description in the digital space for various engineering processes in the physical space is equipped with certain advanced models and algorithms developed by ourselves. For a macroscopic flow problem, we can model it either in the Navier-Stokes scheme, suitable for the injection, transportation and oil-water separation processes, or in the Darcy scheme, suitable for the drainage and sorption processes. Lattice Boltzmann method can also be developed as a special discretization of the Navier-Stokes scheme, which is easy to be coupled with multiple distributions, for example, temperature field, and a rigorous Chapman-Enskog expansion is performed to show the equivalence between the lattice Bhatnagar-Gross-Krook formulation and the corresponding Navier-Stokes equations and other macroscopic models. Based on the mathematical toolpackage, for various practical applications in petroleum engineering related with reservoirs, we can always find the suitable numerical tools to construct a digital twin to simulate the operations, design the facilities and optimize the processes. Β© The Author(s) 2021.
publications-5360 Conference paper 2021 Ping J.-L.; Zhang L.-M.; Zhou C.; Wang C.-B.; Lu C. The Verification for RRC System of Nuclear Power Plant Based on Digital Twins Technologies Lecture Notes in Electrical Engineering 10.1007/978-981-16-3456-7_74 This paper takes verification of the reactor RRC system of a pressurized water reactor nuclear power plant as the research object. This paper determines the modelling scope of the RRC system according to the characteristics of the RRC system, modeled its process systems and control systems, and eventually constructed the digital Twins system of the RRC system, which realizes the dynamic closed-loop verification of the RRC system. The paper gives examples of implementation for typical accidents. The research results eventually achieve the purpose of accurate verification of the RRC system, and improve the safety and economy level of nuclear power plants, and play an important role in improving the safety and economy level of nuclear power plant operation. Β© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.