Adaptive Grid Research Articles

An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

Covid has resulted in millions of deaths worldwide, making it crucial to develop fast and safe diagnostic methods to control its spread. Chest X-Ray imaging can diagnose pulmonary diseases, including Covid. Most research studies have developed single convolution neural network models ignoring the advantage of combining different models. An ensemble model has higher predictive accuracy and reduces the generalization error of prediction. We employed an ensemble of Multi Deep Neural Networks models for Covid.19 classification in chest X-Ray scans using Multiclass classification (Covid, Pneumonia, and Normal). We improved the accuracy by identifying the best parameters using the sklean Grid search technique and implementing it with the Optimized Weight Average Ensemble Model, which allows multiple models to predict. Our ensemble model has achieved 95.26% accuracy in classifying the X-Ray images; it demonstrates potential in ensemble models for diagnosis using Radiography images.

A numerical method for sharp-interface simulations of multicomponent alloy solidification

We present a computational method for the simulation of the solidification of multicomponent alloys in the sharp-interface limit. Contrary to the case of binary alloys where a fixed point iteration is adequate, we hereby propose a Newton-type approach to solve the non-linear system of coupled PDEs arising from the time discretization of the governing equations, allowing for the first time sharp-interface simulations of the multialloy solidification. A combination of spatially adaptive quadtree grids, Level-Set Method, and sharp-interface numerical methods for imposing boundary conditions is used to accurately and efficiently resolve the complex behavior of the solidification front. The convergence behavior of the Newton-type iteration is theoretically analyzed in a one-dimensional setting and further investigated numerically in multiple spatial dimensions. We validate the overall computational method on the case of axisymmetric radial solidification admitting an analytical solution and show that the overall method's accuracy is close to second order. Finally, we perform numerical experiments for the directional solidification of a Co-Al-W ternary alloy with a phase diagram obtained from the PANDAT™ database and analyze the solutal segregation dependence on the processing conditions and alloy properties.

A Novel Clustering Method Based on Adjacent Grids Searching.

Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.

Efficient solutions of preconditioned large‐scale systems for simulative aircraft cabin noise assessment

AbstractThe rising number of passengers transported by aircraft leads to more flight traffic, further increasing the environmental impact of the aviation sector. In order to combat the growing environmental impact, the Cluster of Excellence Sustainable and Energy Efficient Aviation of TU Braunschweig aims to advance research toward climate neutral aviation, especially with the design of newly developed aircraft. In the conscience of passengers, the focus is also shifted toward a healthy and comfortable travel experience. One of the main factors influencing these aspects is noise inside the aircraft cabin. A lower noise impact can help increase the technology acceptance and further push toward more sustainable airborne transport solutions. In order to assess cabin noise with moderate effort in an early design stage, simulative methods such as the finite element method (FEM) are deployed. These have the advantage of yielding important insights into the noise distribution inside the aircraft cabin without having to have a built prototype present. Since methods like the FEM are of a wave‐resolving nature, results contain high‐resolution wave propagation information of the sound waves, created, for example, by the excitation of the turbulent boundary layer on the aircraft's outer skin. Additionally, a direct auralization at seat positions is possible. More powerful computers have further improved the usage of models for an early design noise assessment. However, the more elaborate a model is, the more degrees of freedom (DOFs) it yields as final system of equations. Finally, since the FEM is of wave‐resolving nature and in order to depict sound waves accurately, a fine discretization of 10 nodes per frequency dependent wave length is needed. This leads to a massive increase in DOFs for higher frequencies and finally yielding a large‐scale model, which entails huge computational efforts. This makes the usage of adaptive grids very convenient. In this contribution, adaptive grids entail two main aspects: frequency and domain adaptive discretization. Since higher frequencies lead to shorter wavelengths and therefore finer mesh sizes, it is feasible to adaptively discretize the model in the frequency domain, while also adaptively discretizing the different domains of the models itself, because different materials lead to different wavelengths. However, the resulting system of equations might be ill‐posed. Therefore, this contribution aims to shed light on an efficient solving process of these adaptively discretized large‐scale models with preconditioning and adequate solver choice.

An adaptive hybrid grids finite-element approach for plane wave three-dimensional electromagnetic modeling

In unstructured finite element (FE) plane wave electromagnetic modeling, the goal-oriented mesh adaptive method based on posterior errors is often used to optimize the mesh to a state where a high-precision numerical solution can be obtained with fewer elements. However, for high-frequency data, this method tends to generate a large number of small elements in the shallow near-surface area, which increases the computational burden of FE solution. To solve this problem, we developed an improved goal-oriented hybrid mesh adaptive method. This method realizes the coupling of prismatic elements and tetrahedral elements in FE analysis and can establish FE system equations without suspension nodes. Numerical example studies illustrate how the proposed method uses triangular prism elements to divide the near-surface area and greatly improves the computational efficiency of plane wave electromagnetic modeling. For high-frequency modeling, the proposed hybrid grid reduces the number of required elements, improves the numerical accuracy, reduces the iterations of adaptive grids refinement, and improves the efficiency of the adaptive numerical solution. For low-frequency forward calculations, the hybrid grid maintains the same solution efficiency as traditional adaptive numerical simulation based on pure tetrahedrons.

Numerical simulation of the horizontal rotating cylinder and the air entrainment near the free surface

The flow around a cylinder near a free surface contains many physical phenomena and mechanisms, especially when the cylinder is moving. In this paper, the two-dimensional flow of a rotating cylinder near the free surface is simulated. The dynamic behavior of the rotating cylinder and the flow characteristics such as force coefficient, vortex structure, and air entrainment under different depth ratios, Fr numbers, and rotation ratios are discussed. In such problems, the rotational motion is rarely mentioned. The two-phase model is the sharp volume of the fluid method based on the two-dimensional incompressible Navier–Stokes equation. Combined with the spatially adaptive four-octree grid, the gas–liquid interface is reconstructed with the high-density ratio (ρwater/ρair = 816). The results show that, at α ≤ 2.0, the combined effect of the free surface and the rotation causes the wake vortex to produce a positive upper and negative lower distribution, which is contrary to the single condition. For the rotating cylinder, the existence of a free surface leads to the stable vortex layer at a low rotation ratio (α = 1.0), which only occurs at the high rotation ratio (2 < α < 4.2) without free surface. For the force coefficients, the simultaneous existence of the free surface and the rotation obviously changes the value and periodicity of the coefficients, which is different from the single condition. As for the air entrainment, it can be divided into two categories within the selected parameters: vortex entrainment caused by the parallel free surface and wake jet entrainment. In the latter model, the entailed bubbles have a wider distribution in space due to the influence of shedding vortices.

Fuzzy control based adaptive rotational inertia VSG grid integration research

Compared with the traditional inverter grid-connected control strategy, virtual synchronous generator (VSG) has received attention because it can effectively reduce the current shock to the grid caused by load fluctuation during grid-connected process by adding rotational inertia and damping parameters. A fuzzy control scheme is added to the rotor equation of motion of VSG to establish fuzzy rules by finding the relationship between frequency fluctuation and rotational inertia during grid connection. To improve the immunity of the frequency converter to interference during grid connection, and then improve the stability of the output power in the grid-connection process. The feasibility of the proposed method is verified by simulink model construction for simulation and comparison with the conventional virtual synchronous generator system.

Variability of the slow solar wind: New insights from modelling and PSP-WISPR observations

Aims. We analyse the signature and origin of transient structures embedded in the slow solar wind, and observed by the Wide-Field Imager for Parker Solar Probe (WISPR) during its first ten passages close to the Sun. WISPR provides a new in-depth vision on these structures, which have long been speculated to be a remnant of the pinch-off magnetic reconnection occurring at the tip of helmet streamers. Methods. We pursued the previous modelling works of Réville et al. (2020, ApJ, 895, L20; 2022, A&A, 659, A110) that simulate the dynamic release of quasi-periodic density structures into the slow wind through a tearing-induced magnetic reconnection at the tip of helmet streamers. Synthetic WISPR white-light (WL) images are produced using a newly developed advanced forward modelling algorithm that includes an adaptive grid refinement to resolve the smallest transient structures in the simulations. We analysed the aspect and properties of the simulated WL signatures in several case studies that are typical of solar minimum and near-maximum configurations. Results. Quasi-periodic density structures associated with small-scale magnetic flux ropes are formed by tearing-induced magnetic reconnection at the heliospheric current sheet and within 3 − 7 R⊙. Their appearance in WL images is greatly affected by the shape of the streamer belt and the presence of pseudo-streamers. The simulations show periodicities on ≃90 − 180 min, ≃7 − 10 h, and ≃25 − 50 h timescales, which are compatible with WISPR and past observations. Conclusions. This work shows strong evidence for a tearing-induced magnetic reconnection contributing to the long-observed high variability of the slow solar wind.

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by designing a validity evaluation function, selecting initial clustering centers by distance, constructing a multidimensional feature vector, and simplifying calculations using triangle inequality. An adaptive image grid division method based on perspective mapping and inverse perspective mapping with a corrected basic equation is proposed for constructing a dataset of wheat lodging directions. The improved k-means algorithm and direction dataset are used to construct a bag of visual words. Based on scale-invariant feature transform, pyramid word frequency, histogram intersection kernel, and support vector machine, the wheat lodging directions were detected in the grid. The proposed method was verified through experiments with images acquired on an intelligent combine harvester. Compared with single-level word frequencies with existing and improved k-means, the mean accuracy of wheat lodging direction detection by pyramid word frequencies with improved k-means increased by 6.71% and 1.11%, respectively. The average time of detection using the proposed method was 1.16 s. The proposed method can accurately and rapidly detect wheat lodging direction for combine harvesters and further realize closed-loop control of intelligent harvesting operations.

Multi-objective high-dimensional multi-fractional-order optimization algorithm for multi-objective high-dimensional multi-fractional-order optimization controller parameters of doubly-fed induction generator-based wind turbines

Aiming at the operation control of doubly-fed induction generator-wind energy systems, this study firstly introduces multi-dimensional information feedback and fractional-order theory and designs a high-dimensional multi-fractional-order controller in the rotor side converter. Secondly, for the tuning optimization of controller parameters of doubly-fed induction generators, this study proposes a high-dimensional multi-fractional-order optimization. Finally, this study proposes a multi-objective high-dimensional multi-fractional-order optimization algorithm for the multi-objective optimization problems of control parameters tuning and optimization. This study adopts an adaptive grid strategy to update non-dominated solutions for external archives with crowded conditions. Furthermore, this study adopts a roulette strategy to select the optimal global position in the iterative process. This study utilizes multi-objective test functions to verify the effectiveness of the proposed multi-objective optimization. The Pareto optimal solutions and five types of statistical data from two indexes show that the proposed multi-objective optimization has improved convergence, diversity, and operational stability. Compared with five algorithms, the proposed multi-objective optimization can obtain Pareto optimal solutions with uniform distribution and good diversity in the multi-objective tuning of control parameters of the designed multi-objective controllers for doubly-fed induction generators. Therefore, decision-makers can choose the control parameters according to the actual needs. The test results show that the designed multi-objective controller optimized by the proposed multi-objective optimization has more accurate power tracking ability and superior control performance.

Research on Global Optimization Algorithm of Integrated Energy and Thermal Management for Plug-In Hybrid Electric Vehicles.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO-DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO-DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29-84.67%, and the accuracy of the global optimal solution is improved by 0.94-16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO-DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68-23.74% under various driving conditions, which increases the energy-saving potential by 0.55-3.26% compared to just doing the energy management.

A Cartesian-octree adaptive front-tracking solver for immersed biological capsules in large complex domains

We present an open-source adaptive front-tracking solver for biological capsules in viscous flows. The membrane elastic and bending forces are solved on a Lagrangian triangulation using a linear Finite Element Method and a paraboloid fitting method. The fluid flow is solved on an octree adaptive grid using the open-source platform Basilisk. The Lagrangian and Eulerian grids communicate using an Immersed Boundary Method by means of Peskin-like regularized Dirac delta functions. We demonstrate the accuracy of our solver with extensive validations: in Stokes conditions against the Boundary Integral Method, and in the presence of inertia against similar (but not adaptive) front-tracking solvers. Excellent qualitative and quantitative agreements are shown. We then demonstrate the robustness of the present solver in challenging cases featuring extreme membrane deformations, very large computational domains and high volume fractions. Moreover, we illustrate the capability of the solver to simulate inertial capsule-laden flows in complex STL-defined geometries, opening the door for bioengineering applications featuring large three-dimensional channel structures. The source code and all the test cases presented in this paper are freely available.

Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations

Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations

Revisit on energy flow: accurate predictions and analysis of heatlines for thermal convection within enclosures of various configurations

PurposeA few earlier studies presented infeasible heatline trajectories for natural convection within annular domains involving an inner circular cylinder and outer square/circular enclosure. The purpose of this paper is to revisit and illustrate the correct heatline trajectories for various test cases.Design/methodology/approachGalerkin finite element based methodology and space adaptive grid have been used to simulate natural convective flows within the annular domains. The prediction of heatlines involves derivatives at the nodes, which are evaluated based on finite element basis functions and contributions from neighboring elements.FindingsThe heatlines in the earlier work indicate infeasible heat flow paths such as heat flow from one portion to the other of isothermal hot walls and heat flow across the adiabatic walls. Current results illustrate physically consistent heat flow paths involving perpendicularly emerging heatlines from hot to cold walls for conductive transport, long heat flow paths around the closed-loop heatline cells for convective transport and parallel layout of heatlines to the adiabatic walls. Results also demonstrate complex heatlines involving multiple flow vortices and complex flow structures.Originality/valueCurrent work translates heatfunctions from energy flux vectors, which are determined by using basis sets. This work demonstrates the expected heatline trajectories for various scenarios involving conductive and convective heat transport within enclosures with an inner hot object as a first attempt, and the results are precursors for the understanding of energy flow estimates.

Adaptive grid deformation method for CFD application to hull optimization

This work introduces a new grid deformation method for an efficient CFD-based optimization of ship hull forms. The method uses a two-level point transformation technique to manipulate grid points with a small number of design points. At the first level, generic B-spline is employed to transfer grid points based on movements of control points sampled inside a control box, ensuring accuracy and smoothness of surface modification. At the second level, Radial Basis Function with Wendland’s C2 continuity is adopted to interpolate movements of control points based on relatively few design points. It is shown to be effective in preserving good mesh quality and efficiency. The method is applied to the deformation of hull surfaces for ship models KVLCC2 and KCS, and to investigate the effects of bulbous bow on calm-water resistance with fixed Lpp. A regression model is proposed for ship length, location of buoyancy, wet surface area, and displacement. Numerical results show that the present method is well-suited for CFD-based hull form optimization with better efficiency.

Computationally efficient direction of arrival estimation using adaptive grid selection

AbstractThe authors propose a computationally efficient approach to estimate the directions of arrival of far‐field sources impinging on a sensor array. The proposed estimator is formed using a sparse reconstruction framework, employing a novel adaptive grid selection technique to reduce the dimensionality of the used dictionary matrix. The method further makes use of a SPICE‐inspired dictionary to adaptively select an appropriate regularisation parameter and to select the active grid set. An adaptive stepsize selection strategy is also introduced to reduce computation complexity further. The proposed method allows for accurate direction‐of‐arrival (DOA) estimates using even a single snapshot, also from weak sources. Numerical simulations indicate that the method offers preferable performance in comparison to existing state‐of‐the‐art DOA estimation algorithms.

A privacy-preserving trajectory data synthesis framework based on differential privacy

Trajectory data contains spatio-temporal information about a large number of moving objects, and analysis of this data can yield a lot of valuable information to support a variety of application requirements related to moving objects. However, publishing unprotected trajectory data carries the risk of revealing personal privacy. In the existing approaches, the utility of published trajectory data is sacrificed for privacy protection. In this paper, we propose a differential privacy mechanism DP-Std for data publication in synthetic trajectory databases. first, the DP-Std mechanism constructs a density- and distance-aware adaptive grid structure for discrete geographic regions, and adds noise in the process to satisfy differential privacy. Second, DP-Std extracts feature distributions to ensure the utility of synthetic trajectories. Third, DP-Std constructs a mobility model that samples intermediate location points. And then the trajectory dataset is synthesized by differential privacy distribution and mobility model. Finally, we conduct experiments on two datasets to evaluate our framework. The results show that DP-Std outperforms other feature-based trajectory synthesis methods in terms of data utility and achieves a trade-off between privacy and utility with strict privacy protection.

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm

Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (VSMM) were proposed. This article proposes two VSMM algorithms for tracking maritime targets performing a coordinated turn at constant speed. These are algorithms with a variable set of models based on adaptive grid and switching grid methods.Aim. To develop an adaptive trajectory tracking algorithm that uses a constant turn model to track maneuvering surface objects.Materials and methods. The resulting algorithm is based on the theory of grid adaptation in multi-model estimation methods and is used to estimate the components of the coordinate and velocity vectors of surface maneuvering targets. The algorithm efficiency was evaluated using computer statistical modeling in the MATLAB environment.Results. The structure of an adaptive VSMM algorithm was described. Simulations were carried out to confirm the algorithm efficiency. In the considered simulation scenarios, the algorithm produces effective estimates of the coordinate vectors and speed of surface maneuvering targets.Conclusion. Adaptive algorithms improve the efficiency of target tracking in comparison with multi-model algorithms with a fixed structure, at the same time as saving computational resources.

Near-continuum, hypersonic oxygen flow over a double cone simulated by direct simulation Monte Carlo informed from quantum chemistry

A large-scale, fully resolved direct simulation Monte Carlo (DSMC) computation of a non-equilibrium, reactive flow of pure oxygen over a double cone is presented. Under the simulated near-continuum conditions, the computational demands are shown to be significant because of the wide range of length scales that must be resolved. Therefore, robust grid adaption capabilities and efficient parallelization of the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) code that is utilized in this work are essential. The thermochemical and transport collision models were selected for efficiency and simplicity. First-principles data, obtained from the highly accurate direct molecular simulation method, were used to inform the collision models’ parameters. Importantly, because SPARTA implements molecular collision models using collision-specific energies, the resulting macroscopic relaxation rates were evaluateda posteriorivia zero-dimensional heat bath simulations. The comparisons of surface properties, namely heat flux and pressure, show very close agreement with previous computational fluid dynamics (CFD) results. Differences with the measurements were found to be similar to the CFD simulations. The unresolved discrepancy with the measurements could be due to inconsistent free stream conditions with the actual experimental data or missing physical phenomena altogether, for example atomic and molecular oxygen electronically excited states, three-dimensional effects, or more complex gas–surface interactions. As shown in this work, the advantages of obtaining a DSMC particle solution for these flows reside in the method's ability to be directly informed from first principles and to seamlessly describe internal energy non-equilibrium for all modes. With the advent of exascale computing and beyond, particle methods will be an increasingly important tool to verify the validity of physical assumptions in reduced-order models via fully resolved, experimental-scale simulations, down to the level of molecular-level distributions.