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 Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-4961 Article 2023 Guan G.; Liu-Wang J.; Chen X.; Shi L. Roughness estimation methods of hydrodynamic digital twin models for canal systems; [θΎ“ζ°΄ζΈ η³»ζ°΄ε_x008a_¨ε_x008a_›ζ•°ε­—ε­η”_x009f_模ε_x009e_‹η³™η_x008e_‡δΌ°θ®΅ζ–Ήζ³•] Shuikexue Jinzhan/Advances in Water Science 10.14042/j.cnki.32.1309.2023.06.008 A segmented estimation method of roughness based on the variation of hydraulic radius and estimation accuracy is proposed in order to realize the real- time and high- fidelity roughness estimation of hydrodynamic digital twin (DT) models. This method considers the spatial variability of the roughness value in the longitudinal direction of canals. Based on canal segmentation, two different estimation frameworks, the independent estimation method and joint estimation method, are proposed. The ensemble Kalman filter algorithm is applied to estimate the roughness of each canal segment online based on the limited observed water levels. The results show that the two estimation methods can improve the accuracy of the model by 20% β€”50% . In addition, the independent estimation method is suitable for a complex canal system with a small error accumulation, while the joint estimation method is suitable for simple canals with unavailable observations. The proposed method can be used for parameter estimation and variable updating of hydrodynamic DT models, and provide a reference for the construction of DT water networks. Β© 2023 China Water Power Press. All rights reserved.
publications-4962 Article 2023 Bessenbacher V.; Schumacher D.L.; Hirschi M.; Seneviratne S.I.; Gudmundsson L. Gap-Filled Multivariate Observations of Global Land–Climate Interactions Journal of Geophysical Research: Atmospheres 10.1029/2023JD039099 The volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap-filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap-filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state-of-the-art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land–atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995–2020 globally with gap-free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)-Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate-resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA-CCI burned area, and snow cover fraction as well as 2-m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state-of-the-art interpolation. The gap-filled data set shows high correlations with ERA5-Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA-CCI only agree with the reanalysis product ERA5-Land trends after gap-filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations. © 2023. The Authors.
publications-4963 Article 2023 Li W.; Cai J.; Lu H.; Wang J.; Cai L.; Tang Z.; Li J.; Wang C. Constructing a probability digital twin for reactor core with Bayesian network and reduced-order model Annals of Nuclear Energy 10.1016/j.anucene.2023.110016 In constructing a digital twin for a nuclear reactor core, it is important to consider the influence of randomness from various sources. Data assimilation (DA) can combine time distribution observations with dynamic models to approximate the real state of a physical system. Machine learning (ML) and DA share similarities under the Bayesian framework, and using probabilistic ML may provide a way to improve or replace current DA techniques. This paper proposes using a probabilistic ML as Bayesian neural network (BNN) to solve an inverse problem of core monitoring and demonstrates its feasibility through a pressurized water reactor core simulation analysis. Model order reduction technology is also analyzed, and the feasibility and benefit of using it to achieve core monitoring under steady-state conditions is preliminarily verified and discussed. Future work will focus on improving estimation and prediction models under transient operating conditions by unifying DA and ML under the Bayesian framework. Β© 2023 Elsevier Ltd
publications-4964 Article 2023 Hens B.; Sarcevica I.; Tomaszewska I.; McAllister M. Digitalizing the TIM-1 Model Using Computational Approaches─Part Two: Digital TIM-1 Model in GastroPlus Molecular Pharmaceutics 10.1021/acs.molpharmaceut.3c00423 A TIM-1 model is an in vitro gastrointestinal (GI) simulator considering crucial physiological parameters that will affect the in vivo drug release process. The outcome of these experiments can indicate the critical bioavailability attributes (CBAs) that will impact the fraction absorbed in vivo. The model is widely used in the nonclinical stage of drug product development to assess the bioaccessible fraction of drugs for numerous candidate formulations. In this work, we developed a digital TIM-1 model in the GastroPlus platform. In a first step, we performed validation experiments to assess the luminal concentrations and bioaccessible fractions for two marker compounds. The digital TIM-1 was able to adequately reflect the luminal concentrations and bioaccessible fractions of these markers under different prandial conditions, confirming the appropriate integration of mass transfer in the TIM-1 model. In a second set of experiments, a case example with PF-07059013 was performed, where luminal concentrations and bioaccessible fractions were predicted for 200 and 1000 mg doses under fasted and achlorhydric conditions. Experimental and simulated data pointed out that the achlorhydric effect was more pronounced at the 1000 mg dose, showing a solubility-limited dissolution and, consequently, decreased bioaccessible fraction. Toward future applications, the digital TIM-1 model will be thoroughly applied to explore a link between in vitro and in vivo outcomes based on more case examples with model compounds with the access of TIM-1 and plasma data. Ideally, this digital TIM-1 can be directly used in GastroPlus to explore an in vitro-in vivo correlation (IVIVC) between the fraction dissolved (digital TIM-1 settings) and the fraction absorbed (human PBPK settings). © 2023 American Chemical Society.
publications-4965 Review 2023 Bakhtiari V.; Piadeh F.; Behzadian K.; Kapelan Z. A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management Sustainable Cities and Society 10.1016/j.scs.2023.104958 Cutting-edge digital visualisation tools (CDVT) are playing an increasingly important role in improving urban flood risk management. However, there is a paucity of comprehensive research examining their role across all stages of urban flood risk management. To address, this study conducts an integrated critical review to identify the application of CDVT and assess their contribution to the prevention, mitigation, preparation, response, and recovery stages of flood risk management. The results show that virtual reality, augmented reality, and digital twin technologies are the primary CDVT used in urban flood visualisation, with virtual reality being the most frequently used. The focus of urban flood visualisation studies has been primarily on preparation and mitigation stages. However, there is a need to investigate the application of these technologies in the entire urban water cycle. Furthermore, there is potential for greater adoption of digital twin, especially in simulating urban flood inundation and flood evacuation routes. Integrating real-time data, data-driven modeling, and CDVT can significantly improve real-time flood forecasting. This benefits stakeholders and the public by enhancing early warning systems, preparedness, and flood resilience, leading to more effective flood risk management and reduced impacts on communities. Β© 2023 The Author(s)
publications-4966 Conference paper 2024 Liu P.; Xing J.; Li Y.; Miller C.; Tang P. Knowledge Sharing and Workforce Engagement Using Digital Twins-Based Simulations and Extended Reality for Process Operations Computing in Civil Engineering 2023: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023 10.1061/9780784485231.081 Water treatment plants (WTPs) encompass complex processes, presenting challenges for both control algorithms and human operators. Traditional anomaly detection often requires human intervention, and both parties exhibit limitations when handling system anomalies. This study introduces a "digital twin" model of water systems, enhanced with an extended reality (XR) interface, designed to capture and replicate operators' anomaly detection strategies. The objective is to overcome the difficulties associated with tracing and interpreting operators' behaviors, which are often due to inadequate process pattern mining methods. The proposed approach is fourfold: (1) a water system digital twin equipped with simulation models and a virtual reality (VR) interface, (2) process capture for inspection and operation, (3) visual trajectory pattern mining for knowledge discovery, and (4) an augmented reality interface to guide workers. Experimental results indicate that for simulated anomaly inspection and detection, the computer's recall was 0.712, whereas human operators achieved a recall of 0.851. A combination of both yielded a higher classification recall of 0.885. Knowledge transfer predicated on specific observations could address pressing WTP challenges, including a shortage of experienced operators and unreliable fault recovery systems. Β© 2024 Computing in Civil Engineering 2023: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023. All rights reserved.
publications-4967 Article 2024 Chen L.; Zhao K.; Tao W.-Q. Research on one-dimensional digital twin algorithm of plate heat exchanger Numerical Heat Transfer; Part A: Applications 10.1080/10407782.2023.2222906 Based on the differential idea, this article establishes two segmented algorithms which are the linear equations method and iterative method for solving the one-dimensional digital twin of the temperature field and pressure field of plate heat exchanger (PHE). The segmented algorithms are to consider the change of the physical properties of the fluid along the flow direction, which makes the solution of the temperature field and pressure field more accurate than that without segmentation. It can still get other important parameters besides temperature and is easy to converge with a few iterative steps. When the boundary conditions are known, it can accurately solve the heat transfer, wall temperature, convective heat transfer coefficient, temperature field and pressure field of fluid. Under six groups of test conditions, the heat exchange, temperature and pressure calculated by the two algorithms are compared with the commercial software Flownex, when the number of segments is 100, the average maximum error of each segment is not more than −0.39%, −0.97%, and 0.18%, respectively. To balance accuracy and calculation speed, this article also explores the impact of the number of segments on accuracy. It is found that when the number of segments increases to 10, the impact on the calculation accuracy can be ignored. Compared with Flownex, these two algorithms have smaller errors, and the solution speed of solving linear equations method is faster than that of iteration. It provides application value for emerging digital twin technology, data center chilled water system and other projects and industries that need to accurately control the outlet temperature and wall temperature of PHE. © 2023 Taylor & Francis Group, LLC.
publications-4968 Conference paper 2024 Marty P.; Mitcham T.; Ali R.; Boehm C.; Duric N.; Fichtner A. Towards Elastic Bone Characterization in Transcranial Ultrasound Progress in Biomedical Optics and Imaging - Proceedings of SPIE 10.1117/12.3006769 Constructing a physics-augmented digital twin of the skull is imperative for a wide range of transcranial ultrasound applications including ultrasound computed tomography and focused ultrasound therapy. The high impedance contrast as well as the acoustic-elastic coupling observed between soft tissue and bone increase the complexity of the ultrasound wavefield considerably, thus emphasizing the need for waveform-based inversion approaches. This work applies reverse time migration in conjunction with the spectral-element method to an in vitro human skull to obtain a starting model, which can be used for full-waveform inversion and adjoint-based shape optimization. Two distinct brain phantoms are considered where the cranial cavity of the in vitro human skull was filled with (1) homogeneous water and (2) gelatin with two cylindrical inclusions. A 2D slice through the posterior of the skull was collected using a ring-like aperture consisting of 1024 ultrasound transducers with a bandwidth of approximately 1 MHz to 3 MHz. Waveform-based reverse time migration was then used to resolve the inner and outer contours of the skull from which a conforming hexahedral finite-element mesh was constructed. The synthetically generated measurements which are obtained by solving the coupled acoustic-viscoelastic wave equation are in good agreement with the observed laboratory measurements. It is demonstrated that using this revised wave speed model for recomputing the reverse time migration reconstructions allows for improved localization of the gelatin inclusions within the cranial cavity. Β© 2024 SPIE.
publications-4969 Book chapter 2024 Wood D.A. Real-time monitoring and optimization of drilling performance using artificial intelligence techniques: a review Sustainable Natural Gas Drilling: Technologies and Case Studies for the Energy Transition 10.1016/B978-0-443-13422-7.00017-9 Multiple supervised and unsupervised artificial intelligence techniques have been adapted and applied for real-time drilling monitoring and optimization purposes. This chapter describes these methods and provides examples and case studies of how they are being applied to improve the efficiency and sustainability of drilling techniques. Data preprocessing, feature selection/importance, statistical and correlation methods, prediction reliability, multi-K-fold analysis, and annotated-confusion-matrix techniques all contribute to improved interpretation of these AI methods, particularly machine, and deep learning. Artificial intelligence (AI) is at the forefront of providing real-time monitoring and predictions required by digital-twin and automated drilling strategies. There are many supervised and unsupervised AI methods used with real-time drilling data inputs for various monitoring and prediction purposes. This chapter describes the various machine and deep learning supervised techniques and the unsupervised clustering and discriminant analysis methods providing examples of how they are applied to solve drilling-related issues. Data screening and performance assessment techniques are identified together with statistical and correlation analysis required to establish relationships among the variables involved. AI enhancement techniques, including multi-K-fold cross-validation, reliability modeling, feature selection, and importance analysis, refine the performance of the AI methods and establish their generalizability. Examples of how these techniques are being used in drilling research and development are provided throughout the review. Three case studies are presented to illustrate the advances being made and the challenges faced. These address prediction rheology and filtration parameters in water-based drilling fluids, rate of penetration predictions from mud-log and petrophysical data, and classification of loss of circulation from a large database of recorded drilling variables with imbalanced class distributions. The benefits are highlighted by recent AI prediction interpretation techniques involving multi-K-fold cross-validation, annotated confusion matrices, and optimizer-assisted feature selection. Β© 2024 Elsevier Inc. All rights reserved.
publications-4970 Conference paper 2024 Wang Y.; Kong X.; Guo K.; Zhao C.; Zhao J. Intelligent extraction method of soil and water conservation terrace measures in Loess Plateau based on deep learning artificial neural network Proceedings of SPIE - The International Society for Optical Engineering 10.1117/12.3019869 The severe soil erosion on the Loess Plateau is the root of the Yellow River problem, and the mathematical model of soil erosion is the core of intelligent soil and water conservation. How to extract soil and water conservation measures accurately, quickly, and quantitatively is of great significance for developing the parameterization method of soil and water conservation measures in the soil erosion model and quantitatively revealing the influence of soil and water conservation measures allocation on runoff and sediment yield process. It is the direct demand to support the research and development of distributed soil erosion models, soil erosion prediction, and digital twin watershed construction. Based on analyzing the current high-resolution remote sensing image data and deeply learning the development frontier of artificial intelligence, this paper studies and explores the technology and method of intelligent extraction of terrace measures underpinned by an artificial neural network algorithm model under deep learning. The main research results are as follows: (1) The current deep learning theory development and model structure are deeply studied. (2) The intelligent extraction algorithm of high-resolution remote sensing images based on depth learning is developed. (3) Accuracy analysis of the model results are carried out. The research results of the project will provide basic data for regional soil erosion prevention and control, and at the same time, provide technical support for high-precision parameter acquisition and calibration of distributed soil erosion model, which has important theoretical practice and application value. Β© 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.