Precise Simulation Research Articles

A hyper-optimisation method based on a physics-informed machine learning and point clouds for a flat plate solar collector

This paper presents a new way to hyper-optimise a flat plate solar collector using a combination of regenerated point clouds, constructal theory, and physics-informed machine learning (PIML). The behaviour of the flat plate solar collector is studied as solar radiation and ambient temperature change, using both precise numerical simulation and PIML. The novel hyper-optimisation method integrates these two approaches to improve the performance of the solar collector. In this method, the volume of fluid and solid structure of the flat plate solar collector (FPSC) is transformed into point clouds based on constructal theory. The point clouds are then regenerated into a continuous and uniform 3D geometry using optimised parameters. To put the modified version of the flat plate solar collector (FPSC) into practice, a computational method is used to generate a training data set for machine learning, specifically for neural networks. After thoroughly verifying the computational results, the PIM is trained using the generated training data set. This study marked the first time that a regular computational method is replaced with PIML output to reduce the computational cost of prediction. In the second layer of calculation, a deep neural network is used to make predictions based on the outputs generated by PIML. Seven independent parameters are used to predict heat transfer and efficiency over time, including time, heat flux, mass flow rate, inlet temperature, number of pairs and clusters, and volume fraction of nanofluid, while 16 hidden layers and 63 learnable neurons are engaged in this prediction layer. The geometry matrix is redefined by constructal theory principles in a series of iteration loops to generate the first flat plate solar collector based on constructal theory (CTFPSC). The results indicated that the hyper-optimisation method could reduce calculation costs by 18.31% compared with the regular computational method. In addition, the results reveal that maximum outlet temperatures are possible when Nc > 3 and Np> 5.

Skidding characteristics of urban rail vehicle axle-box bearings considering surface waviness on races

As one of the core transmission components, axle-box bearings have been widely applied in urban rail vehicles and played a crucial role in their safe and stable operation. No matter in the starting and stopping stage or stable operation stage of the vehicle, the skidding phenomenon in axle-box bearings exists and impacts their own dynamic characteristics. Furthermore, as one of the main shape errors of the bearing surface, waviness may cause fluctuations for skidding characteristics of axle-box bearings. This study investigates the skidding dynamic analysis of axle-box bearings, taking into account race surface waviness. Firstly, a dynamics model for double-row cylindrical roller bearings is established, considering the cage flexibility and friction forces between different components. Then, this model is coupled with a three-dimension vehicle–track model, allowing for more precise dynamic simulation of axle-box bearings under actual running conditions. Furthermore, an excitation related to race surface waviness is added to the coupling model through time-varying displacement and contact stiffness. At last, the influence of race surface waviness on skidding characteristics of axle-box bearings is analyzed. The effectiveness of the proposed model is validated through theoretical analysis and field tests. Simulation results demonstrate that race surface waviness can impact the cage and roller slip ratio of axle-box bearings. Overall, this study provides valuable insights into the skidding dynamic analysis of axle-box bearings, highlighting the importance of considering race surface waviness in designing and maintaining axle-box bearings.

Superconducting detector magnets for high energy physics

Various superconducting detector solenoids for particle physics have been developed in the world. The key technology is the aluminum-stabilized superconductor for almost all the detector magnets in particle physics experiments. The coil fabrication technology is also important and it has advanced along with the conductor technology, such as the inner coil winding technique, indirect cooling, transparent vacuum vessel, quench protection scheme using pure aluminum strips and so on. The detector solenoids design study is in progress for future big projects in Japan and Europe, that is, ILC (International Linear Collider), FCC (Future Circular Collider) and CLIC (Compact LInear Collider), based on the technologies established over many years. The combination of good mechanical properties and keeping a high RRR is a key point for the development of Al-stabilized conductor. The present concern for the detector solenoid development is to have been nearly losing the key technologies and experiences. Nowadays, there are no industrial companies having the capacity to manufacture such aluminum stabilized superconductor. Complementary efforts are seriously required to re-realize and validate the performance required in the future projects in collaboration with worldwide institutes and industries. Some mid-scale physics experiments required detector solenoids wound with not aluminum stabilized conductor but conventional copper stabilized conductor. The specific requirement is to control the magnetic field distribution precisely, and the efforts to realize the requirement are on going with regard to the magnetic field design technology with high precision simulation, coil fabrication technology and so on.

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step activation function (STEPAF)-SPKAF, Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF, achieving very good results.

Estimation of Electromagnetic Field Penetration into Concrete Buildings Using a Theoretical Approach Considering External Environmental Factors

In this paper, we propose a theoretical approach to estimate the power level of electromagnetic waves radiated into a structure by a specific external source. The target structure is a multistory building on a university campus that is used primarily for academic purposes and is much larger than the target wavelength. To verify the accuracy and efficiency of the proposed theoretical approach, measurements were carried out and a commercially available simulation tool, Wireless Insite, was adopted. We then analyzed the influence of an area of vegetation as an external environmental factor that could affect the radiated electromagnetic waves because of its location in front of the target structure. For this, a precise simulation environment was designed to derive the quantitative values of the electromagnetic attenuation caused by the external environmental factor. Furthermore, those values were applied to the theoretical approach. The results of the theoretical approach accounting for the external environmental factor were similar to those of the actual measured results. The results were also similar to those of the simulation tool, Wireless Insite, but the theoretical approach provided more efficient analysis results in terms of time consumption and computer resources.

A graphical user interface for calculating the arterial input function during dynamic positron emission tomography

Purpose. Dynamic positron emission tomography (dPET) requires the acquisition of the arterial input function (AIF), conventionally obtained via invasive arterial blood sampling. To obtain the AIF non-invasively, our group developed and combined two novel solutions consisting of (1) a detector, placed on a patient’s wrist during the PET scans to measure the radiation leaving the wrist and (2) a Geant4-based Monte Carlo simulation software. The simulations require patient-specific wrist geometry. The aim of this study was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, and measure the wrist features needed to calculate the AIF. Methods. The GUI elements were implemented using Qt5 and VTK-8.2.0. The user imports a patient’s wrist ultrasound scans, measures the radial artery and veins’ surface and depth to model a wrist phantom, then specifies the radioactive source used during the dPET scan. The phantom, the source, and the number of decay events are imported into the Geant4-based Monte Carlo software to run a simulation. In this study, 100 million decays of 18F and 68Ga were simulated in a wrist phantom designed based on an ultrasound scan. The detector’s efficiency was calculated and the results were analyzed using a clinical data processing algorithm developed in a previous study. Results. The detector’s total efficiency decreased by 3.5% for 18F and by 51.7% for 68Ga when using a phantom based on ultrasound scans compared to a generic wrist phantom. Similarly, the data processing algorithm’s accuracy decreased when using the patient-specific phantom, giving errors greater than 1.0% for both radioisotopes. Conclusions. This toolkit enables the user to run Geant4-based Monte Carlo simulations for dPET detector development applications using a patient-specific wrist phantom. Leading to a more precise simulation of the developed detector during dPET and the calculation of a personalized AIF.

The Suppression of Hump Instability inside a Pump Turbine in Pump Mode Using Water Injection Control

The occurrence of hump instability in pump mode within a pump turbine poses a significant challenge to the safe and stable operation of Pumped Storage Power Plants (PSPP). To achieve more precise numerical simulations, this paper establishes a weakly compressible model of water based on the Tait equation. Using this model, it is discovered that the onset of hump instability is closely linked to an increase in hydraulic losses induced by stalled rotation within the diffuser. Then, a flow control approach employing water injection into the guide vanes of a pump turbine is proposed in order to suppress flow instabilities and optimize the hump region. The findings reveal that the water injection approach can mitigate hydraulic losses, suppress unstable structures, and diminish the pulsation amplitude within the diffuser, ultimately delaying the emergence of the hump region to lower flow mass conditions. This study is helpful in widening the range of the safe and stable operation of pump turbines in pump mode.

GIR dataset: A geometry and real impulse response dataset for machine learning research in acoustics

Acoustics play a significant role in our everyday lives, influencing our communication, well-being, and perception of space. Fast and precise acoustics simulation is crucial for the accurate design of real spaces by architects and acousticians and maximises the user’s immersion in virtual and augmented reality environments. Computer simulation techniques can help to simulate and analyse acoustics. However, their cumbersome, computationally expensive, and often inaccurate results discourage most architecture practices from including acoustic evaluation in their design workflow and prevent real-time accurate audio synthesis in virtual reality. Recent advancements in Machine Learning (ML) and particularly Deep Learning offer compelling solutions to address the above problems. ML methods require large datasets for training, and existing datasets are either not large enough, contain synthetic data, or are not suitable for room acoustics research. This paper presents the GIR Dataset, a dataset of 920’712 real Impulse Responses (IRs) of 312 architectural geometries for the study of early reflections from diffusive surfaces. The paper provides a detailed description of the GIR Dataset’s content and an ML use-case example. The dataset and the code described in this paper are open-sourced.

Discontinuous codimension-two bifurcation in a Vlasov equation.

In a Vlasov equation, the destabilization of a homogeneous stationary state is typically described by a continuous bifurcation characterized by strong resonances between the unstable mode and the continuous spectrum. However, when the reference stationary state has a flat top, it is known that resonances drastically weaken and the bifurcation becomes discontinuous. In this article we analyze one-dimensional spatially periodic Vlasov systems, using a combination of analytical tools and precise numerical simulations to demonstrate that this behavior is related to a codimension-two bifurcation, which we study in detail.

A Precise Bare Simulation Approach to the Minimization of Some Distances. I. Foundations

In information theory -- as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -- many flexibilizations of the omnipresent Kullback-Leibler information distance (relative entropy) and of the closely related Shannon entropy have become frequently used tools. To tackle corresponding constrained minimization (respectively maximization) problems by a newly developed dimension-free bare (pure) simulation method, is the main goal of this paper. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). As a side effect, we also derive an innovative way of constructing new useful distances/divergences. To illustrate the core of our approach, we present numerous solved cases. The potential for widespread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences and entropies in various different research fields (which may also serve as an interdisciplinary interface).

Verification of neutronics and thermal-hydraulic coupled system with pin-by-pin calculation for PWR core

As an important part of the digital reactor, the pin-by-pin wise fine coupling calculation is a research hotspot in the field of nuclear engineering in recent years. It provides more precise and realistic simulation results for reactor design, operation and safety evaluation. CORCA-K a nodal code is redeveloped as a robust pin-by-pin wise neutronics and thermal-hydraulic coupled calculation code for pressurized water reactor (PWR) core. The nodal green's function method (NGFM) is used to solve the three-dimensional space-time neutron dynamics equation, and the single-phase single channel model and one-dimensional heat conduction model are used to solve the fluid field and fuel temperature field. The mesh scale of reactor core simulation is raised from the nodal-wise to the pin-wise. It is verified by two benchmarks: NEACRP 3D PWR and PWR MOX/UO2. The results show that: 1) the pin-by-pin wise coupling calculation system has good accuracy and can accurately simulate the key parameters in steady-state and transient coupling conditions, which is in good agreement with the reference results; 2) Compared with the nodal-wise coupling calculation, the pin-by-pin wise coupling calculation improves the fuel peak temperature, the range of power distribution is expanded, and the lower limit is reduced more.

Accurate and Fast Deep Learning Dose Prediction for a Preclinical Microbeam Radiation Therapy Study Using Low-Statistics Monte Carlo Simulations.

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50μm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame. The long computation times inhibit the ability to perform dose optimization in treatment planning or apply online image-adaptive radiotherapy techniques to MRT. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiotherapy options such as MRT. In this work, the successful application of a fast and accurate ML dose prediction model for a preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the tumor target in rat patients. A CT imaging dataset is used to generate digital phantoms for each patient. Augmented variations of the digital phantoms are used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulation data are used to train the ML model to predict the energy depositions in the digital phantoms. The low-noise MC simulations data are used to test the predictive power of the ML model. The predictions of the ML model show an agreement within 3% with low-noise MC simulations for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumor) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumor) in the case of the peak dose prediction. The successful use of high-noise MC simulations for the training, which are much faster to produce, accelerates the production of the training data of the ML model and encourages transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.

Kernel method for gravity forward simulation in implicit probabilistic geologic modeling

Gravity is one of the most widely used geophysical data types in subsurface exploration. In the recent developments of stochastic geologic modeling, gravity data serve as an additional constraint to the model construction. The gravity data can be included in the modeling process as the likelihood function in a probabilistic joint inversion framework and allow the quantification of uncertainty in geologic modeling directly. A fast but also precise forward gravity simulation is essential to the success of the probabilistic inversion. Hence, we have developed a gravity kernel method, which is based on the widely adopted analytical solution on a discretized grid. As opposed to a globally refined regular mesh, we construct local tensor grids for individual gravity receivers, respecting the gravimeter locations and the local sensitivities. The kernel method is efficient in terms of computing and memory use for mesh-free implicit geologic modeling approaches. This design makes the method well suited for many-query applications, such as Bayesian machine learning using gradient information calculated from automatic differentiation. Optimal grid design without knowing the underlying geometry is not straightforward before evaluating the model. Therefore, we further provide a novel perspective on a refinement strategy for the kernel method based on the sensitivity of the cell to the corresponding receiver. Numerical results are presented and found superior performance compared to the conventional spatial convolution method.

Numerical Simulation of a Striated Piezoresistive MEMS Pressure Sensor on Circular Silicon Diaphragm: A Finite Element Method-Based Study

Micro-Electro-Mechanical Systems (MEMS) technology has been heavily incorporated into fabrication of sensors due to its undoubtful advantages such as efficient form-factor, low-power consumption and less bulk manufacturing cost. One of the first sensors to undergo transformation in the micro-scale has been the pressure sensor. Pressure sensors are used excessively in various fields such as automobile, aerospace, aviation, biomedical, consumer electronics, etc. Various researches are being conducted to optimize the design of MEMS-based pressure sensors to meet the specific requirements of different fields. This work presents a piezoresistive pressure sensor design with striated-shaped piezoresistors instead of the conventional bar-shaped piezoresistors. Such a configuration allows positioning the short arms of the striated design effectively on the narrow high stress regions. The piezoresistors are embedded on a circular diaphragm. The performance advantage of such a design is validated through analytical models and numerical simulations. Finite Element Analysis (FEA) is deployed using COMSOL Multiphysics software to achieve precise simulation results. The results are then compared to the existing literature. The proposed design achieves a sensitivity of 40.365 mV/V/MPa under a pressure range of 0–1 MPa. The proposed design performs better than existing literature and can be used in applications that require high sensitivity over a long pressure range, for example, in barometric altimeters.

Numerical simulations on the performance of optical-acoustic sensors of minimal dimensions

This paper presents analytical and numerical methods for developing optical-acoustic transducers of minimal dimensions. One can find acoustic sensors used as microphones in various electronic devices such as smartphones or smartwatches. Therefore, it is highly desirable to minimize their size while ensuring high-quality sound reception. The optical-acoustic sensor relies on laser detection of membrane vibrations and consists of a membrane that vibrates in the presence of an acoustic field and reflects the radiation emitted by the laser back to the laser. We focus on methods to optimize the membrane's design and the cavity (back volume) that separates the laser from the membrane. The back volume compliance significantly affects the sensitivity of the membrane. In addition, it is a noise source due to acoustic and viscous damping. Using calculations and simulations, we show the possibilities of reducing the membrane size and the air-filled back volume size while achieving the desired acoustic properties. We employ analytical calculations for the mechanical vibration of the diaphragm, back-volume compliance and resistance, and precise FEM simulations of the interaction between membrane vibration and the acoustic field. We build on similar techniques used for micromachined capacitive microphones, but we apply these methods newly to a specific setup of backplate-less optical-acoustic sensors. Based on the theoretical results, we can conclude that optical-acoustic devices achieve the same maximum noise level with smaller dimensions than the current industry standard.

Dependence of hopping electrical resistivity on concentration and localization radius, simulation study

The exponential dependence of resistivity on concentration and on electron localization radius in hopping electron transport is of special significance. It is the main clue in favor of hopping conductance mechanism. This dependence was studied experimentally, theoretically and in simulations. So far, the simulations were carried out in the Miller–Abrahams resistor network approximation. In this work, a more precise simulation is presented, which takes into account electron correlations in a fully connected network of sites, and achieves averaging by simulation of supercells. Although occasionally a quantitative agreement with existing reports is achieved, the excellent agreement between the percolation theory and some existing simulations, is a coincidence. The new results are interpreted using percolation theory. There are indications on the important role of the prefactor of resistivity in interpreting the slope of the semi-logarithmic plot of resistivity vs average separation of localization sites. The influence of the finite temperature is investigated. The simulations are carried out assuming a doped crystalline semiconductor as the hopping medium, however the results are applicable to other systems with the identical dependencies of the hopping rate on hopping distance.

Quantitative Evaluation of the Effects of Heat Island on Building Energy Simulation: A Case Study in Wuhan, China

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building’s energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly “site-specific climate data” to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation.

A comprehensive investigation of wetting distribution pattern on sloping lands under drip irrigation: A new gradient boosting multi-filtering-based deep learning approach

Accurate estimation of the wetting distribution pattern (WDP) around the emitters of a drip irrigation system in sloping lands can minimize surface runoff losses by determining the placement status of plants and emitters. In this study, both experimental and computational efforts were made to estimate the WDP in sloping lands with drip irrigation. 486 sets of laboratory experiments were conducted and a series of soil characteristics data were collected. Particularly, the upstream wetting radius (R−), downstream wetting radius (R+), and wetting depth (D) were measured and further used as the target variables of three modeling scenarios. In the modeling effort, a new hybrid framework, consisting of a light gradient boosting machine (LightGBM) and best subset regression (BSR) integrated with bidirectional recurrent neural network (Bi-RNN), was developed for precise simulation of WDP. The main model (i.e., Bi-RNN) was compared with the Elman recurrent neural network (ERNN) and bagging regression tree (BGRT) in the advanced multi-filtering framework for all the scenarios. In the first stage, the LightGBM tree-based feature selection filtered the significant predictors in each scenario. In the second stage, the three best possible input combinations using the N predictors selected in the first stage were extracted among 2N possible combinations via the BSR strategy. The performances of the models were evaluated by using different statistical metrics. It was demonstrated that Bi-RNN achieved the highest accuracy in all the hybrid models for the three scenarios, followed by the ERNN and BGRT models. Also, a resampling bootstrap-based uncertainty analysis proved that the developed multistage-filtering strategy before the deep learning model feeding decreased the uncertainty associated with input combination effects. By determining the placement status of plants and emitters, the proposed framework can effectively reduce surface runoff losses.

The Fractional Analysis of a Nonlinear mKdV Equation with Caputo Operator

In this study, we aim to provide reliable methods for the initial value problem of the fractional modified Korteweg–de Vries (mKdV) equations. Fractional differential equations are essential for more precise simulation of numerous processes. The hybrid Yang transformation decomposition method (YTDM) and Yang homotopy perturbation method (YHPM) are employed in a very simple and straightforward manner to handle the current problems. The derivative of fractional order is displayed in a Caputo form operator. To illustrate the conclusion given from the findings, a few numerical cases are taken into account for their approximate analytical solutions. We looked at two cases and contrasted them with the actual result to validate the methodologies. These techniques create recurrence relations representing the proposed problem’s solution. It is possible to find the series solutions to the given problems, and these solutions have components that converge to precise solutions more quickly. Tables and graphs are used to describe the new results, which demonstrate the present methods’ adequate accuracy. The actual and estimated outcomes are demonstrated in graphs and tables to be quite similar, demonstrating the usefulness of the proposed approaches. The innovation of the current work resides in the application of effective methods that require less calculation and achieve a greater level of accuracy. Additionally, the suggested approaches can be applied in the future to resolve other nonlinear fractional problems, which will be a scientific contribution to the research community.

Calibration Parameter of Soil Discrete Element Based on Area Difference Method

Existing calibration methods for contact parameters of soil discrete elements are primarily based on a single physical property of soil (e.g., natural accumulation angle and shear characteristics). To solve this limitation, a calibration method based on the area difference method was proposed in accordance with the actual operation effect of ridge and furrow opener. The typical clay loam in the middle and lower reaches of the Yangtze River (moisture content of 20.66% ± 1%) was used as the research object. The Hertz–Mindlin with JKR Cohesion contact model was selected, and the static friction factor and rolling friction factor between soil and 65Mn steel were determined by inclined plane mechanics method. Soil tank ridge and furrow opening experiments and quadratic orthogonal rotation combined tests were carried out. MATLAB software was used to carry out polynomial fitting and the constant integral solution to the ridge and the cross-sectional contour data of the furrow obtained from the measured and simulated ridge and furrow opening experiments to establish the regression model of area difference. The optimization function in Design-Expert software was adopted. The optimal parameter combination was obtained by optimizing the regression model with the minimum area difference as the target. Based on the above parameter combination, the relative error of cross-sectional area of the furrow that was obtained by simulation test and soil tank test was 9.15%. Finally, the comparison of the traction resistance of the simulation and the soil tank test at the stable working stage indicated that the relative error between the simulated value and the measured value was 10.4%, which is less than the acceptable error of 15% in general statistics and within a reasonable range. The results showed that the particle movement and mechanical properties of the soil simulation model were basically consistent with the actual soil after parameter optimization and calibration. In this study, a precise discrete element simulation model of the typical clay loam in the middle and lower reaches of the Yangtze River based on the area difference method was constructed, which lays a theoretical basis for later exploration of the interaction mechanism between the typical clay loam and the soil contact components in the middle and lower reaches of the Yangtze River.