Numerical Experiments Research Articles

Stabilized equal lower-order finite element methods for simulating Brinkman equations in porous media

This paper demonstrates the mixed formulation of the Brinkman problem using linear equal-order finite element methods in porous media modelling. We introduce Galerkin least-squares (GLS) and least-squares (LS) finite element methods to address the incompatibility of finite element spaces, treating velocity, pressure, and vorticity as independent variables. Theoretical analysis examines coercivity and continuity, providing error estimates. Demonstrating resilience in theoretical findings, these methods achieve optimal convergence rates in the L 2 norm by incorporating stabilization terms with low-order basis functions. Numerical experiments validate theoretical predictions, showing the effectiveness of the GLS method and addressing finite element space incompatibility. Additionally, the GLS method exhibits promising capabilities in handling the Brinkman equation at low permeability compared to the LS method. The study reveals an increase in the average pressure difference in the Brinkman problem compared to the Stokes equations as the inlet velocity rises, providing insights into the behaviour of Brinkman equations.

Impact of Summer North Atlantic Sea Surface Temperature Tripole on Precipitation over Mid–High-Latitude Eurasia

Abstract Eurasia is a sensitive and high-risk region for global climate changes, where climate anomalies significantly influence natural ecosystems, human health, and economic development. The North Atlantic tripole (NAT) sea surface temperature anomaly is crucial to interannual precipitation variations in Eurasia. Several studies have focused on the link between the NAT and climate anomalies in winter and spring. However, the mechanism by which the summer NAT impacts climate anomalies in Eurasia remains unclear. This study examines how the NAT impacts interannual variations of summer precipitation in mid–high-latitude Eurasia. Precipitation variations are associated with the atmospheric teleconnection triggered by the NAT. When the NAT is in its positive phase, the anomalous atmospheric diabatic heating over the North Atlantic excites an equivalent-barotropic Rossby wave train response that propagates eastward toward Eurasia, resulting in atmospheric circulation anomalies over the region. The combined effects of atmospheric circulation, radiative forcing, and water vapor transport anomalies lead to decreased precipitation across northern Europe and central Eurasia, with higher precipitation anomalies over northeast Asia. Finally, numerical experiments verify that the summer NAT excites atmospheric teleconnections that propagate downstream, affecting precipitation anomalies in mid–high-latitude Eurasia. This study provides a scientific basis for predicting Eurasian summer precipitation and strengthening disaster management strategies.

Cooperative Load Transportation with Quadrotors using Adaptive RISE Control

The problem of transporting cable-suspended payloads using two quadrotors is analyzed in this work. The system kinematics are treated using a virtual structure formation controller, which generates the acceleration references commanded to the aerial vehicles. The disturbances caused by the payload are treated as unmodeled disturbances and a novel robust integral of the sign of the error (RISE) controller is proposed, guaranteeing the asymptotic convergence of the tracking errors. A model reference adaptive control is also incorporated into the RISE controller, combining the advantages of adaptive and robust control. The proposal is validated by numerous experiments using two quadrotors to transport a bar-shaped payload in adverse conditions. The results allow the conclusion that the proposed system is capable of performing transportation and orientation tasks with the payload, subject to translational accelerations up to 3.5 m/s2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}.

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We present a method for computing actions of the exponential-like φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}-functions for a Kronecker sum K of d arbitrary matrices Aμ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_\\mu $$\\end{document}. It is based on the approximation of the integral representation of the φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}-functions by Gaussian quadrature formulas combined with a scaling and squaring technique. The resulting algorithm, which we call phiks, evaluates the required actions by means of μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu $$\\end{document}-mode products involving exponentials of the small sized matrices Aμ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_\\mu $$\\end{document}, without forming the large sized matrix K itself. phiks, which profits from the highly efficient level 3 BLAS, is designed to compute different φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}-functions applied on the same vector or a linear combination of actions of φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}-functions applied on different vectors. In addition, thanks to the underlying scaling and squaring techniques, the desired quantities are available simultaneously at suitable time scales. All these features allow the effective usage of phiks in the exponential integration context. In fact, our newly designed method has been tested in popular exponential Runge–Kutta integrators of stiff order from one to four, in comparison with state-of-the-art algorithms for computing actions of φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}-functions. The numerical experiments with discretized semilinear evolutionary 2D or 3D advection–diffusion–reaction, Allen–Cahn, and Brusselator equations show the superiority of the proposed μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu $$\\end{document}-mode approach.

Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods

Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models in which simulating synthetic data is very expensive. In this article, we propose an early rejection Markov chain Monte Carlo (ejMCMC) sampler based on Gaussian processes to accelerate inference speed. We early reject samples in the first stage of the kernel using a discrepancy model, in which the discrepancy between the simulated and observed data is modeled by Gaussian process (GP). Hence, synthetic data is generated only if the parameter space is worth exploring. We demonstrate through theory, simulation experiments, and real data analysis that the new algorithm significantly improves inference efficiency compared to existing early-rejection MCMC algorithms. In addition, we employ our proposed method within an ABC sequential Monte Carlo (SMC) sampler. In our numerical experiments, we use examples of ordinary differential equations, stochastic differential equations, and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We develop an R package that is available at https://github.com/caofff/ejMCMC.

Diffraction tomography for incident Herglotz waves

Abstract Diffraction tomography is an inverse scattering technique used to reconstruct the spatial distribution of the material properties of a weakly scattering object. The object is exposed to radiation, typically light or ultrasound, and the scattered waves induced from different incident field angles are recorded. In conventional diffraction tomography, the incident wave is assumed to be a monochromatic plane wave, an unrealistic simplification in practical imaging scenarios. In this article, we extend conventional diffraction tomography by introducing the concept of customized illumination scenarios, with a pronounced emphasis on imaging with focused beams. More specifically, we consider incident Herglotz waves and extend the classical Fourier diffraction theorem to this setting. This yields a new two-step reconstruction process which we comprehensively evaluate through numerical experiments.

Human activities increased compound drought and heat events over China during 1961–2014

AbstractCompared to independent drought or extreme heat events, compound drought and heat events (CDHEs) can pose more adverse impacts on ecosystems and human society. As the anthropogenic influence on CDHEs in China remains largely unquantified, we analyse both observed and simulated changes in the occurrence of CDHEs over China from 1961 to 2014, and perform detection and attribution analyses utilizing an optimal fingerprinting method, based on observations, reanalysis data and numerical experiments from seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Upward trends are found in observed CDHEs during 1961–2014 over the majority of China, especially in the last two decades. The increase in CDHEs is largely attributed to human influence. Anthropogenic forcing is detectable in the observed CDHEs changes in the whole China and its eastern region. Furthermore, a three‐signal analysis indicates that greenhouse gases play a key role in increasing CDHEs over China, while natural forcing has a negligible effect. Anthropogenic aerosol emissions also play a detectable role in offsetting the greenhouse gas‐induced increase of CDHEs over China.

Predicting particle catchment areas of deep-ocean sediment traps using machine learning

Abstract. The ocean's biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure deep-carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep due to the influence of ocean circulation on the sinking path of carbon. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep-ocean sediment trap. Our numerical experiments support the predictive performance of the machine learning approach, and surface conditions appear to provide valuable information for accurately predicting the source area, suggesting a potential application with satellite data. We also identify factors that potentially affect prediction efficiency, and we show that the best predictions are associated with low kinetic energy and the presence of mesoscale eddies above the trap. This new tool could provide a better link between satellite-derived sea surface observations and deep-ocean sediment trap measurements, ultimately improving our understanding of the biological-carbon-pump mechanism.

Analysis and computation of an inverse source problem for the biharmonic wave equation

Abstract In this paper, we study the inverse source problem for the biharmonic wave equation. Mathematically, we characterize the radiating and non-radiating sources at a fixed wavenumber. We show that a general source can be decomposed into a radiating source and a non-radiating source. The radiating source can be uniquely determined by Dirichlet boundary measurements at a fixed wavenumber. Moreover, we derive a Lipschitz stability estimate for determining the radiating source. On the other hand, the non-radiating source does not produce any scattered fields outside the support of the source function. Numerically, we propose a novel source reconstruction method based on the Fourier series expansion by multi-wavenumber boundary measurements. The stability of the proposed method is analyzed and numerical experiments are presented to verify the accuracy and efficiency.

Sustainable Development of the China Railway Express under the Belt and Road Initiative: Focusing on Infrastructure Reliability and Trade Facilitation

The China Railway Express (CR Express) is an important component of the Belt and Road Initiative (BRI). The sustainable development of CR Express provides critical support for regional economic integration and promotes a balanced development of the supply chain. Infrastructure reliability and trade facilitation greatly impact the operation of CR Express and are crucial for improving the competitiveness of transnational trade and cross-border efficiency. Inconsistent transportation infrastructure standards and low-efficient transportation service procedures affect the cross-border movement of cargo among countries. This paper integrates quantified metrics of infrastructure reliability and trade facilitation into a spatial friction model based on the electrical resistance theory, estimating the impact of these factors on the transportation flow of CR Express. Additionally, three scenarios of infrastructure reliability and trade facilitation are established for the four trade routes from Zhengzhou to Hamburg. Numerical experiments show that compared with inland river routes and traditional ocean routes, infrastructure reliability and trade facilitation significantly influence the transport flow of CR Express. These research results can provide a reference for the improvements of CR Express transportation efficiency and the simplification of customs clearance processes, potentially promoting the sustainable development of the CR Express supply chain to some extent.

A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.

Sequential model identification with reversible jump ensemble data assimilation method

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods.

An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks

Monte Carlo tree search (MCTS) has been gaining increasing popularity, and the success of AlphaGo has prompted a new trend of incorporating a value network and a policy network constructed with neural networks into MCTS, namely, NN-MCTS. In this work, motivated by the shortcomings of the widely used upper confidence bounds applied to trees (UCT) policy, we formulate the node selection problem in NN-MCTS as a multistage ranking and selection (R&S) problem and propose a node selection policy that efficiently allocates a limited search budget to maximize the probability of correctly selecting the best action at the root state. The value and policy networks in NN-MCTS further improve the performance of the proposed node selection policy by providing prior knowledge and guiding the selection of the final action, respectively. Numerical experiments on two board games and an OpenAI task demonstrate that the proposed method outperforms the UCT policy used in AlphaGo Zero and MuZero, implying the potential of constructing node selection policies in NN-MCTS with R&S procedures. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72325007, 72250065, and 72022001], and a PKU-Boya Postdoctoral Fellowship 2406396158. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0307 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0307 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

IDENTIFICATION OF AN ARBITRARY MOVING AXISYMMETRIC LOAD ACTING ON A CYLINDRICAL SHELL

Various types of external non-stationary loads can act on various structural elements and cylindrical shells of finite length in particular distributed and concentrated, stationary and moving load. When using various external load identification methods, the type of external load is usually known. In prac- tice this is not always the case. The purpose of the study is to develop a method for identifying an arbitrary axisymmetric load acting on an elastic cy- lindrical shell of finite length, which can be used to identify a moving load. To model the non-stationary load of a cylindrical shell, a system of differ- ential equations of the refined Timoshenko theory for medium thickness shells was used. The solution to this system of differential equations is ob- tained by expanding the unknown functions into Fourier series and applying the integral Laplace transform. The solution of the inverse problem was obtained using the theory of integral equations and the Tikhonov regularization method. As a result of the study, a solution of the inverse problem of solid mechanics to identify an arbitrary axisymmetric non-stationary load was obtained. A numerical experiment was carried out to use the developed method to identify a moving non-stationary load acting on a simply supported cylindrical shell of medium thickness. The simulation results indicate a fairly accurate identification of both the change in time and the distribution along the shell of a non-stationary axisymmetric moving load. A method has been developed for identifying an external non-stationary load randomly distributed along a cylindrical shell, which makes it possible to identify a moving load without preliminary information about the type of this load. The developed method allows us to reproduce moving non-stationary loads, which are often encountered in practice, and expand it to other types of structural elements.

Bifurcation Analysis and Optimal Control of an SEIR Model with Limited Medical Resources and Media Impact on Heterogeneous Networks

The spread of epidemic diseases is often considered to be bound up with human behavior. Both treatment and awareness-raising are seen as important preventive measures during an outbreak. In this paper, an SEIR model on heterogeneous network containing saturated treatment function with media information variable is constructed. Through mathematical analysis, the basic reproduction number and equilibriums of the system are derived. The global stability of disease-free equilibrium is further proved based on LaSalle’s invariant principle. It is also found that with a gradual increase of the delayed treatment parameter, the system exhibits a transition from forward bifurcation to backward bifurcation with the variation of the basic reproduction number. Finally, an optimal control problem is formulated with both treatment rate and media information dissemination rate as control variables and solved using the Pontryagin’s maximum principle. All the above results are verified by numerical experiments.

Spare parts provisioning strategy of warranty repair demands for capital-intensive products

The inventory cost of stocking spare parts is a nonnegligible expenditure of providing after-sales service for the manufacturers making capital-intensive products, such as electric vehicles. Especially, for warranty repair service, it is important to manage the spare stock appropriately to satisfy the warranty claims of customers as well as reduce the associated inventory costs. In this paper, we investigate the spare parts inventory issue related to a critical component for under-warranty units of a product. In particular, under the free-replacement warranty policy, failed component will be replaced by a new one by consuming the spare stock. According to the field claim data, we find that the general trend of warranty claims is nonstationary, which will be affected by the product sales and under-warranty failures. Thus, we first propose a model to forecast the time-varying warranty repair demand by explicitly considering the randomness from two major sources, that is, product sales and under-warranty failures. Under the assumptions of Poisson sales process and exponential failure distribution, the closed-form expressions of mean and variance of cumulative warranty repair demand over time are obtained. Because the number of warranty claims in each period is a one-time data, the associated distribution information is unavailable. Then, based on the properties of the demand statistics, we derived a worst-case upper bound for the associated inventory cost and formulate a three-phase finite-horizon spare parts inventory model, which can be used to appropriately address the time-varying warranty claims. Finally, numerical experiments are conducted to investigate the key parameters affecting the optimal decisions where a case study based on real data is presented.

A comparative study of linear type multiple instance learning techniques for detecting COVID-19 by chest X-ray images

AbstractAt the end of 2019, the World Health Organization (WHO) referred that the Public Health Commission of Hubei Province, China, reported cases of severe and unknown pneumonia. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, called COVID-19 (COronaVIrus Disease 2019). Although the definitive COVID-19 diagnosis is made through specific molecular tests, an early diagnosis by imaging became crucial to contain the spread, morbidity and mortality of the pandemic. In such context, chest X-ray radiography, as an element that assists the diagnosis allowing also the follow-up of the disease, plays a very important role since it is the most easily available and least expensive alternative. This work focuses on applying different linear type instance-level Multiple Instance Learning techniques to discriminate between COVID-19 and common viral pneumonia chest X-ray images, which is a difficult task due to the strong similarity characterizing the two classes. A relevant advantage of such approaches is that they are also suitable in terms of interpretability, as they easily allow clinicians to identify abnormal subregions in a positive radiographic image. Numerical experiments have been performed on a set of 200 images, obtaining the following results: accuracy = 95%, sensitivity = 99.29%, specificity = 91.24% and MCC = 0.9. The used algorithms appear promising in practical applications, taking into account their high speed and considering that no particular pre-processing techniques have been employed.

The Drawdown of a Reservoir: Its Effect on Seepage Conditions and Stability of Earth Dams

This article addresses the reliability and safety of an earth dam in the case of a change in the reservoir water level. The water level must often be reduced to remove water or as a response to an emergency situation in the process of operation of a hydraulic structure. Lower water levels change seepage conditions, such as the surface of depression, values and directions of seepage gradients, seepage rates, and volumetric hydrodynamic loading. Practical hydraulic engineering shows that these changes can have a number of negative consequences. Higher seepage gradients can lead to seepage-triggered deformations in the vicinity of the upstream slope of a structure. Hydrodynamic loads, arising during drawdown, reduce the stability of an upstream slope of a dam and cause its failure. Potential consequences of a drawdown can be evaluated by solving the problem of drawdown seepage for the dam body and base. A numerical solution to this problem is based on the finite element method applied using the PLAXIS 2D software package. Results thus obtained are compared with those obtained using the finite element method in the locally variational formulation. A numerical experiment was conducted to analyze factors affecting the value of the maximum seepage gradient and stability of the earth dam slope. Recommendations were formulated to limit the drawdown parameters and to ensure the safe operation of a structure.

Investigating the mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system

Abstract The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.

Cyclic seismic pushover testing of a multi-story underground station

The experimental approach is crucial for investigating the seismic performance and damage process of underground structures. Considering the shortcomings of the 1-g, centrifuge shaking table and monotonic displacement pushover tests, a large-scale cyclic displacement pushover test method is proposed based on the soil-underground structure dynamic interaction and seismic performance quantification system. Taking a two-story three-span subway station structure as the prototype, the cyclic displacement pushover test device was designed for a 1/7-scale multi-story subway station based on the seismic response characteristics of underground structures. The corresponding numerical simulations and experiments were conducted. Typical numerical results (including the seismic damage process, capacity curves of the structural columns, and strain response) and test results (the macroscopic phenomenon of structural damage development, strain response, and deformation response) are interpreted. The results show that the proposed cyclic displacement pushover test is better than the monotonic displacement pushover test, the damage process of the tested station structure conforms to the description of the inter-story drift ratio (IDR) quantification system of seismic performance. Meanwhile, the column has greater strain amplitudes than other components, and the column strain curves reach their peaks before other components. Furthermore, the tested station structure has a similar damage pattern to the Daikai subway station. The reliability and feasibility of the proposed cyclic displacement pushover test method are verified.