Optimal Siting of Rural Settlement Through a GIS-Based Assessment: A Case Study in China

The optimization of spatial allocation of rural settlements in China requires an evaluation of rural land suitability and location optimization for the convenience of rural residents living and working. The main idea behind this study was to propose a new methodology to locate the most suitable sites for rural residents as considering their potential demand and supply. A geographic information system (GIS) based multiple criteria decision-making (MCDM) model was used to identify the most suitable areas and the unsuitable parts. The weighted information value method was used to assigning weight for influential factors. A case study was conducted for Tangjiahui, a hilly area in the central of China, by performing a town-wide suitability assessment. The very high suitability area is 4621.05 hm2, where is close to the road and with a moderate elevation and low slope, which contains 70.29% of residents. Finally, the maximal covering model was performed to determine the most suitable locations. Six sites were selected from 312 potential sites, which cover whole potential demand settlements with an average travel distance of 2.9 km. The result of location optimization is reasonable and therefore the methodology is applicable

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides

Non-intrusive reduced order models and their applications

Reduced order models (ROMs) have become prevalent in many fields of physics as they offer the potential to simulate dynamical systems with substantially increased computation efficiency in comparison to standard techniques. Among the model reduction techniques, the proper orthogonal decomposition (POD) method has proven to be an efficient means of deriving a reduced basis for high-dimensional flow systems. The intrusive ROM (IROM) is normally derived by the POD and Galerkin projection methods. The IROM is appealing for non-linear and linear model reductions and has been successfully applied to numerous research fields. However, IROMs suffer from instability and non-linearity efficiency issues. In addition, they can be complex to code because they are intrusive. In most cases the source code describing the physical system has to be modified in order to generate the reduced order model. These modifications can be complex, especially in legacy codes, or may not be possible if the source code is not available (e.g. in some commercial software). To circumvent these shortcomings, non-intrusive approaches have been introduced into ROMs. The Non-Intrusive ROM (NIROM) is independent of the original physical system. The key contribution of this thesis are: Firstly, three novel NIROMs have been presented in this thesis: POD/Taylor series, POD-Smolyak and POD-RBF (radial basis function). Secondly, two NIROMs with varying material properties have been presented. Thirdly, these newly developed NIROMs were implemented and tested under the framework of an unstructured mesh finite element model (FLUIDITY) and a combined finite-discrete element method based solid model (Y2D). Fourthly, these NIROMs have been used to construct ROMs for multi-scale 3-D free surface flows, multi-phase porous media flows, fluid-structure interaction and blasting problems.Open Acces

A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)

We present a Domain Decomposition Reduced Order Data Assimilation (DD-RODA) model which combines Non-Intrusive Reduced Order Modelling (NIROM) method with a Data Assimilation (DA) model. The NIROM is defined on a partition of the domain in sub-domains with overlapping regions and the DA is defined on a partition of the domain in sub-domains without overlapping regions. This choice allows to avoid communications among the processes during the Data Assimilation phase. However, during the balance phase, the model exploits the domain decomposition implemented in DD-NIROM which balances the results among the processes exploiting overlapping regions. The model is applied to the pollutant dispersion within an urban environment. Simulations are performed using the open-source, finite-element, fluid dynamics model Fluidity

Non-intrusive reduced order modelling of fluid–structure interactions

A novel non-intrusive reduced order model (NIROM) for fluid–structure interaction (FSI) has been developed. The model is based on proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation method. The method is independent of the governing equations, therefore, it does not require modifications to the source code. This is the first time that a NIROM was constructed for FSI phenomena using POD and RBF interpolation method. Another novelty of this work is the first implementation of the FSI NIROM under the framework of an unstructured mesh finite element multi-phase model (Fluidity) and a combined finite-discrete element method based solid model (Y2D).The capability of this new NIROM for FSI is numerically illustrated in three coupling simulations: a one-way coupling case (flow past a cylinder), a two-way coupling case (a free-falling cylinder in water) and a vortex-induced vibration of an elastic beam test case. It is shown that the FSI NIROM results in a large CPU time reduction by several orders of magnitude while the dominant details of the high fidelity model are captured

Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation

This paper presents a novel Ensemble Kalman Filter (EnKF) data assimilation method based on a parameterised non-intrusive reduced order model (P-NIROM) which is independent of the original computational code. EnKF techniques involve the expensive calculations of ensembles. In this work, the recently developed P-NIROM Xiao et al. [40] is incorporated into EnKF to speed up the ensemble simulations. A reduced order flow dynamical model is generated from the solution snapshots, which are obtained from a number of the high fidelity full simulations over the specific parametric space RP. The varying parameter is the background error covariance σ ∈ RP. Using the Smolyak sparse grid method, a set of parameters in the Gaussian probability density function is selected as the training points. The proposed method uses a two-level interpolation method for constructing the P-NIROM using a Radial Basis Function (RBF) interpolation method. The first level interpolation approach is used for generating the solution snapshots and POD basis functions for any given background error covariance while the second level interpolation approach for forming a set of hyper-surfaces representing the reduced system.The EnKF in combination with P-NIROM (P-NIROM-EnKF) has been implemented within an unstructured mesh finite element ocean model and applied to a three dimensional wind driven circulation gyre case. The numerical results show that the accuracy of ensembles and updated solutions using the P-NIROM-EnKF is maintained while the computational cost is significantly reduced by several orders of magnitude in comparison to the full-EnKF

Deep-learning assisted reduced order model for high-dimensional flow prediction from sparse data

The reconstruction and prediction of full-state flows from sparse data are of great scientific and engineering significance yet remain challenging, especially in applications where data are sparse and/or subjected to noise. To this end, this study proposes a deep-learning assisted non-intrusive reduced order model (named DCDMD) for high-dimensional flow prediction from sparse data. Based on the compressed sensing (CS)-Dynamic Mode Decomposition (DMD), the DCDMD model is distinguished by two novelties. Firstly, a sparse matrix is defined to overcome the strict random distribution condition of sensor locations in CS, thus allowing flexible sensor deployments and requiring very few sensors. Secondly, a deep-learning-based proxy is invoked to acquire coherent flow modes from the sparse data of high-dimensional flows, thereby addressing the issue of defining sparsity and the stringent incoherence condition in the conventional CSDMD. The two advantageous features, combined with the fact that the model retains flow physics in the online stage, lead to significant enhancements in accuracy and efficiency, as well as superior insensitivity to data noises (i.e., robustness), in both reconstruction and prediction of full-state flows. These are demonstrated by three benchmark examples, i.e., cylinder wake, weekly-mean sea surface temperature and isotropic turbulence in a periodic square area.Comment: 36 Pages, 23 Figures, 5 Table

Observation and analysis of diving beetle movements while swimming

The fast swimming speed, flexible cornering, and high propulsion efficiency of diving beetles are primarily achieved by their two powerful hind legs. Unlike other aquatic organisms, such as turtle, jellyfish, fish and frog et al., the diving beetle could complete retreating motion without turning around, and the turning radius is small for this kind of propulsion mode. However, most bionic vehicles have not contained these advantages, the study about this propulsion method is useful for the design of bionic robots. In this paper, the swimming videos of the diving beetle, including forwarding, turning and retreating, were captured by two synchronized high-speed cameras, and were analyzed via SIMI Motion. The analysis results revealed that the swimming speed initially increased quickly to a maximum at 60% of the power stroke, and then decreased. During the power stroke, the diving beetle stretched its tibias and tarsi, the bristles on both sides of which were shaped like paddles, to maximize the cross-sectional areas against the water to achieve the maximum thrust. During the recovery stroke, the diving beetle rotated its tarsi and folded the bristles to minimize the cross-sectional areas to reduce the drag force. For one turning motion (turn right about 90 degrees), it takes only one motion cycle for the diving beetle to complete it. During the retreating motion, the average acceleration was close to 9.8 m/s2 in the first 25 ms. Finally, based on the diving beetle's hind-leg movement pattern, a kinematic model was constructed, and according to this model and the motion data of the joint angles, the motion trajectories of the hind legs were obtained by using MATLAB. Since the advantages of this propulsion method, it may become a new bionic propulsion method, and the motion data and kinematic model of the hind legs will be helpful in the design of bionic underwater unmanned vehicles

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with highthroughput alloy development approaches