Article
Mechanics
Romit Maulik, Bethany Lusch, Prasanna Balaprakash
Summary: This study demonstrates that using a combination of convolutional autoencoders (CAEs) and recurrent neural networks effectively overcomes the limitations of the POD-Galerkin technique in advection-dominated nonlinear PDEs. Truncated systems with only a few latent space dimensions can accurately reproduce complex fluid dynamics phenomena, and parameter information can be easily embedded to detect trends in system evolution.
Article
Engineering, Civil
Bonchan Koo, Hyunsoo Kim, Taehyun Jo, Sangwoo Kim, Joon Yong Yoon
Summary: In this study, the POD-Galerkin projection method was used for model order reduction in the quasi-two-dimensional water-hammer problem. By reducing the number of degrees of freedom, the computational burden was alleviated, and the temporal behavior of the POD basis vectors was analyzed. The proposed method was validated using experimental data, and the computational cost and complexity were compared to demonstrate its accuracy and efficiency.
JOURNAL OF HYDRAULIC RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Victor Zucatti, William Wolf
Summary: A data-driven closure modeling approach based on proper orthogonal decomposition (POD) temporal modes is utilized to obtain stable and accurate reduced order models (ROMs) for unsteady compressible flows. Model reduction is achieved through Galerkin and Petrov-Galerkin projection of the non-conservative compressible Navier-Stokes equations. The study involves analysis of a canonical compressible cylinder flow and turbulent flow over a plunging airfoil undergoing dynamic stall, requiring regularization and iterative Tikhonov methodology.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Mathematics, Applied
Xi Li, Yan Luo, Minfu Feng
Summary: In this paper, an efficient proper orthogonal decomposition based reduced-order model (POD-ROM) for nonstationary Stokes equations is proposed. The new scheme combines the classical projection method with POD technique, resulting in low computational costs and improved efficiency.
JOURNAL OF SCIENTIFIC COMPUTING
(2022)
Article
Mechanics
Marc Olbrich, Markus Baer, Kilian Oberleithner, Sonja Schmelter
Summary: This study investigates different cases of slug flow in horizontal pipes to find statistical characteristics of the slugs in time and space, including slug frequencies, averaged slug body length, and an energy representation. The use of snapshot proper orthogonal decomposition with an additional mode coupling algorithm provides accurate and reliable characterization of the slug flow patterns.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2021)
Article
Mathematics, Applied
Li Wang, Zhen Miao, Yao-Lin Jiang
Summary: This paper studies and analyzes new fast computing methods for partial differential equations with variable coefficients, including two kinds of two-sided Krylov enhanced proper orthogonal decomposition (KPOD) methods. The spatial discrete scheme of an advection-diffusion equation is obtained by Galerkin approximation. Then, algorithms based on two-sided KPOD approaches involving block Arnoldi and block Lanczos processes are proposed for the obtained time-varying equations. Moreover, another type of two-sided KPOD algorithm based on Laguerre orthogonal polynomials in frequency domain is provided. The feasibility of four two-sided KPOD algorithms is verified by numerical results with different inputs and setting parameters.
NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Victor Zucatti, William Wolf, Michel Bergmann
Summary: Calibration analysis of reduced-order models (ROMs) was conducted in this work, testing Galerkin and least-squares Petrov-Galerkin (LSPG) methods on compressible flows with disparate temporal scales. A novel calibration strategy for LSPG method was proposed with analysis on two test cases. Results showed stable and accurate ROMs for both cases, and the impact of hyper reduction on LSPG models was evaluated. Different time-marching schemes were assessed, revealing Galerkin models to be more accurate than LSPG models in solving non-conservative form of the Navier-Stokes equations.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Mechanics
Shijun Chu, Chao Xia, Hanfeng Wang, Yajun Fan, Zhigang Yang
Summary: The study reveals that the seal-vibrissa-shaped cylinder has a more stable three-dimensional separation wake, longer vortex formation length, and weaker vortex strength compared to a circular cylinder at a Reynolds number of 20000. The mean drag and fluctuation of the lift coefficient of the seal-vibrissa-shaped cylinder are significantly reduced, and SPOD can extract four typical vortex shedding patterns.
Article
Mechanics
Jie Hou, Alfa Heryudono, Wenzhen Huang, Jun Li
Summary: This article presents the use of the proper generalized decomposition (PGD) method for parametric solutions of full stress fields in heterogeneous materials. PGD enables accurate prediction of the full stress fields including all localized stress concentration patterns.
Article
Computer Science, Interdisciplinary Applications
Philip Pergam, Heiko Briesen
Summary: This study aims to improve the computational efficiency of a complex mathematical cake-filtration model with strong nonlinearities. A hybrid data-driven approach using proper orthogonal decomposition is employed, and optimal, globally defined basis functions are found based on a few sample simulations. The reduced-order model obtained from this approach has a 98% decrease in dimension compared to the full-order model, resulting in a 90% decrease in computational time for solving a benchmark optimization problem. This significant numerical speed-up offers the potential to use the reduced-order model in advanced process control and optimization methods.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Mathematics, Applied
Kun Li, Ting-Zhu Huang, Liang Li, Stephane Lanteri
Summary: In this article, a reduced-order model (ROM) based on the proper orthogonal decomposition (POD) technique is proposed for modeling the interaction between light and nanometer-scale metallic structures described by the system of 3-D time-domain Maxwell's equations coupled to a Drude dispersion model. The ROM is constructed using the singular value decomposition (SVD) method and a Galerkin projection with a second order leap-frog (LF2) time discretization. The stability condition of the ROM is analyzed, showing that it preserves the stability characteristics of the original high dimensional model. Numerical experiments are presented to validate the accuracy and efficiency of the POD-based ROM for 3-D nanophotonic problems.
NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
(2023)
Article
Multidisciplinary Sciences
Davide Lengani, Daniele Petronio, Daniele Simoni, Marina Ubaldi
Summary: A POD-based procedure is developed to identify the necessary tests/experiments for modeling a complex system. By reducing data dimensionality and learning parsimonious efficiency trends, significant time reduction in test execution can be achieved.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2022)
Article
Engineering, Marine
Yan Zheng, Dan Zhang, Tianbo Wang, Hiroka Rinoshika, Akira Rinoshika
Summary: In this study, a hybrid two-dimensional orthogonal wavelet multiresolution and proper orthogonal decomposition technique is developed to analyze the wake flow behind a semi cylinder. The study investigates the modal energy distributions, flow patterns, and PSD distributions of multi-scale flow structures using the proposed technique. The results show the changes in dominance, symmetry, and energy distribution of the flow structures as they vary from large-scale to small-scale.
Article
Mathematics, Applied
Xiaodong Li, Steven Hulshoff, Stefan Hickel
Summary: Proper Orthogonal Decomposition (POD) is crucial for analyzing complex nonlinear systems governed by partial differential equations (PDEs). Traditional POD methods face challenges in storing high-dimensional solutions, leading to the development of incremental Singular Value Decomposition (SVD). To reduce the total computing cost, the proposed enhanced algorithm for incremental SVD incorporates POD mode truncation. The effectiveness of this algorithm is demonstrated through numerical experiments.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Chinchun Ooi, Quang Tuyen Le, My Ha Dao, Van Bo Nguyen, Hoang Huy Nguyen, Te Ba
Summary: This work demonstrates the integration of multiple machine learning models with a reduced order model for modeling the flow past a stationary cylinder. The experiments show that locally interpolating models are more effective for modeling the time-varying characteristics of POD coefficients, and that the coefficients are best modeled using their own previous time values.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS
(2021)
Article
Computer Science, Interdisciplinary Applications
Shady E. Ahmed, Omer San, Diana A. Bistrian, Ionel M. Navon
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS
(2020)
Article
Meteorology & Atmospheric Sciences
J. Steppeler, J. Li, F. Fang, I. M. Navon
METEOROLOGY AND ATMOSPHERIC PHYSICS
(2020)
Article
Engineering, Multidisciplinary
M. Cheng, F. Fang, C. C. Pain, I. M. Navon
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin
Summary: In this study, a method combining DEIM and PC-EnKF is proposed to retrieve initial conditions in a high-dimensional space, achieving satisfactory reconstruction of the initial field with reduced computational cost. The experimental results demonstrate the effectiveness of the proposed algorithm.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Mechanics
Meiling Cheng, Fangxin Fang, I. M. Navon, C. C. Pain
Summary: A real-time predictive deep convolutional generative adversarial network (DCGAN) was developed for flooding forecasting, showing promising results in capturing underlying flow patterns and making accurate predictions. Further evaluation in complex realistic cases is needed for future improvement.
Article
Mathematics, Interdisciplinary Applications
Suraj Pawar, Omer San, Adil Rasheed, Ionel M. Navon
Summary: This work presents a hybrid modeling approach that combines neural network and data assimilation for learning and representing unknown physical processes. By integrating machine learning and data assimilation, a more accurate estimation of system state can be achieved.
GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS
(2021)
Article
Meteorology & Atmospheric Sciences
Meiling Cheng, Fangxin Fang, Ionel M. Navon, Jie Zheng, Xiao Tang, Jiang Zhu, Christopher Pain
Summary: Efficient and accurate real-time forecasting of national spatial ozone distribution is achieved using a hybrid model (VAE-GAN) that combines a generative adversarial network (GAN) with a variational autoencoder (VAE). The VAE-GAN model can decipher complex relationships and provide long lead-time ozone forecasts.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Mechanics
Claire E. Heaney, Zef Wolffs, Jon Atli Tomasson, Lyes Kahouadji, Pablo Salinas, Andre Nicolle, Ionel M. Navon, Omar K. Matar, Narakorn Srinil, Christopher C. Pain
Summary: This paper introduces a new AI-based non-intrusive reduced-order model (AI-DDNIROM) for modeling multiphase flow in pipes. The model, which utilizes domain decomposition, dimensionality reduction, and neural networks for prediction, is capable of handling predictions for significantly larger domains. The research findings demonstrate that the method matches the accuracy of high-fidelity models in prediction.
Article
Mechanics
Shady E. Ahmed, Pedram H. Dabaghian, Omer San, Diana A. Bistrian, Ionel M. Navon
Summary: With the increasing data volumes, there is a need to develop computationally efficient tools for processing and analyzing large datasets. Dynamic mode decomposition (DMD) is a popular technique that can extract valuable information from data. We propose an efficient DMD framework based on sketching algorithms that can accelerate DMD routines.
Article
Physics, Multidisciplinary
Claire E. Heaney, Xiangqi Liu, Hanna Go, Zef Wolffs, Pablo Salinas, Ionel M. Navon, Christopher C. Pain
Summary: A data-driven non-intrusive reduced-order model (NIROM) is proposed in this study, which is capable of predicting unseen scenarios through novel training data sampling and domain decomposition. Experimental results show that the method achieves success in a 2D test case and demonstrates its generalization ability by maintaining predictive realism in a larger domain with different building arrangements.
FRONTIERS IN PHYSICS
(2022)
Article
Engineering, Multidisciplinary
Jinlong Fu, Dunhui Xiao, Rui Fu, Chenfeng Li, Chuanhua Zhu, Rossella Arcucci, Ionel M. Navon
Summary: This paper proposes a physics-data combined machine learning method for non-intrusive parametric reduced-order modeling in small-data regimes. The method combines dimension reduction through proper orthogonal decomposition with establishing reliable mappings between system parameters and reduced coefficients. It demonstrates high prediction accuracy, strong generalization capability, and small data requirements.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
S. Ashwin Renganathan, Vishwas Rao, Ionel M. Navon
Summary: Estimating the probability of failure in aerospace systems is crucial for flight certification and qualification. This study proposes a method that uses models of multiple fidelities to efficiently estimate failure probability. The method combines multiple models using multifidelity Gaussian process models and utilizes a sequential acquisition function for experiment design. The results demonstrate that the proposed approach predicts failure boundary and probability more accurately with a fraction of the computational cost compared to using a single high-fidelity model. Moreover, the sequential approach guarantees convergence to the true failure boundary with high probability.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mostafa Abbaszadeh, Mehdi Dehghan, Ionel Michael Navon
Summary: This paper discusses the development of a fast and robust numerical method for simulating a system of fractional PDEs, using finite difference and spectral Galerkin methods with reduced-order technique. The stability and convergence properties of this new technique are analyzed, and examples are provided to validate the theoretical results.
ENGINEERING WITH COMPUTERS
(2022)
Proceedings Paper
Mathematical & Computational Biology
D. A. Bistrian, G. Dimitriu, I. M. Navon
APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES (AMITANS 2020)
(2020)
Article
Engineering, Civil
M. Cheng, F. Fang, T. Kinouchi, I. M. Navon, C. C. Pain
JOURNAL OF HYDROLOGY
(2020)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)
