Refinement Strategy Research Articles

Higher-degrees Hybrid Non-uniform Subdivision Surfaces

Higher-degrees Hybrid Non-uniform Subdivision Surfaces

Scene-aware Foveated Rendering.

We propose a new scene-aware foveated rendering method, which incorporates the scene awareness and characteristics of the human visual system into the mapping-based foveated rendering framework. First, we generate the conservative visual importance map that encodes the visual features of the scene, visual acuity, and gaze motion. Second, we construct the pixel size control map using a convolution kernel method. Third, we utilize the pixel size control map to guide the foveated rendering. At last, a temporal coherent refinement strategy is used to maintain the smooth foveated rendering for the adjacent frames. Compared to the state-of-the-art mapping-based foveated rendering methods using the same compression ratio, our method achieves smaller MSE, higher PSNR, and SSIM in the fovea, periphery, salient regions, and the whole image. We also conducted user studies, and the results proved that the perceptual quality of our method has a high visual similarity with the around truth rendered with the full resolution.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

Adaptive constraint-guided surrogate enhanced evolutionary algorithm for horizontal well placement optimization in oil reservoir

In the face of escalating global energy demands, this study introduces an Adaptive Constraint-Guided Surrogate Enhanced Evolutionary Algorithm (ACG-EBS) for optimizing horizontal well placements in oil reservoirs. Addressing the complex challenge of maximizing oil production, the ACG-EBS integrates geological, engineering, and economic considerations into a novel optimization framework. This algorithm stands out for its adept navigation through a complex and discrete decision space of horizontal well placements, an area where traditional methods often encounter challenges. Key innovations include the Adaptive Constraint Initialization Mechanism (ACIM) and the Evolutionary Constraint-Tailored Candidate Refinement strategy (ECTCR), which collectively elevate the feasibility of candidate solutions. An enhanced balance strategy harmonizes comprehensive and niche surrogate models, optimizing the balance between exploration and exploitation. Through testing on both two-dimensional and three-dimensional reservoir models, the ACG-EBS has proven highly effective in identifying optimal well placements that align with field deployment realities and maximize economic returns. This contribution significantly supports the ongoing evolution of oilfield development optimization, showcasing the algorithm's potential to enhance oil production and economic outcomes.

Ionisation of atoms determined by kappa refinement against 3D electron diffraction data.

Conventional refinement strategies used for three-dimensional electron diffraction (3D ED) data disregard the bonding effects between the atoms in a molecule by assuming a pure spherical model called the Independent Atom model (IAM) and may lead toan inaccurate or biased structure. Here we show that it is possible to perform a refinement going beyond theIAM with electron diffraction data. We perform kappa refinement which models charge transfers between atoms while assuming a spherical model. We demonstrate the procedure by analysing five inorganic samples; quartz, natrolite, borane, lutecium aluminium garnet, and caesium lead bromide. Implementation of kappa refinement improved the structure model obtained over conventional IAM refinements and provided information on the ionisation of atoms. The results were validated against periodic DFT calculations. The work presents an extension of the conventional refinement of 3D ED data for a more accurate structure model which enables charge density information to be extracted.

Chloride-salt separation type nanofiltration membranes for efficient crude salt refinement

Application of unrefined crude salt not only leads to a serious equipment scaling and a low product quality, but also increases operational risks and poses health hazards. Therefore, it is essential to refine crude salt prior to its use. However, traditional methods of crude salt refinement are energy-intensive and probably produce additional chemical by-products. In this work, membrane technology was used to accomplish the refinement of crude salt due to its unique characteristics, such as environmentally friendly and lower energy consumption. To improve the separation performance, the membranes were fabricated by simply adjusting the aqueous monomer concentration. For single-salt feed, the membrane (M10) prepared with a high ratio of 10 exhibited excellent salt rejection due to the formation of a thicker and denser PA layer. And it owned a superior MgCl2/NaCl selectivity of ∼22.0, demonstrating that the membrane can achieve outstanding selectivity for mono-/divalent ions. The selectivity of Mg2+/Na+ increased to ∼23.5 for bi-salt feed and further improved to 66.2 for multi-salt feed due to the stronger charge shielding effect. Most importantly, the membrane was also successfully applied in the refinement of crude salt. When using crude salt (NaCl 30 g/L, purity 0.90) as feed, the M10 exhibited both excellent retention of bivalent ions and permeation of univalent ions, resulting in the corresponding Mg2+/Na+ selectivity up to ∼251.6 and the tremendous enhancement in the Na + purity of ∼0.997. This work offers a feasible strategy for crude salt refinement, and could expand to some other potential applications such as resource utilization in the salinization industry and zero-liquid discharge of industrial wastewater.

Fatigue crack growth in functionally graded materials using an adaptive phase field method with cycle jump scheme

Functionally graded materials provide versatility in adjusting the volume fractions of constituent materials to meet specific design requirements. However, this customization often introduces mode-mixity at the crack tip, posing challenges in predicting fracture under cyclic loading with discrete approaches and computationally expensive with conventional phase-field fracture models. To address these issues, this paper introduces an adaptive phase-field fracture formulation with cycle jump scheme to elegantly predict fatigue crack nucleation and growth in functionally graded materials. Within this framework, the effective properties at a point are estimated using the Mori–Tanaka homogenization scheme, while the crack growth due to cyclic load is captured by incorporating an additional fatigue degradation parameter. Moreover, the computational efficiency of the proposed framework is improved through an adaptive mesh refinement and explicit cycle jump scheme. The adaptive refinement scheme utilizes an error indicator derived from both the displacement solution and phase-field variable. The adaptive refinement scheme is integrated with efficient quadtree decomposition, which generates a hierarchical mesh structure. Hanging nodes resulting from the quadtree decomposition are efficiently handled using a polygonal finite element method. The proposed framework is validated against experimental and numerical results reported in the literature. Furthermore, we investigate the fatigue crack growth resistance across a broad range of material gradation directions, gaining valuable insights and identifying functionally graded materials with high fatigue resistance.

PVT2DNet: Polyp segmentation with vision transformer and dual decoder refinement strategy

Colorectal carcinoma is a prevalent malignancy worldwide. Accurate polyp segmentation, along with endoscopic resection, can significantly reduce its incidence and mortality. Most polyp segmentation neural networks are CNN-based and single decoder strategy architectures, which learn limited robust representations. In this paper, we propose a novel network with the vision transformer and dual decoder refinement strategy called PVT2DNet to overcome some limitations of current networks and achieve more precise automated polyp segmentation. The PVT2DNet adopts a pyramid vision transformer encoder and enhances the multi-level features with the context-enhanced module (CEM). Moreover, instead of directly feeding features into a single decoder, we introduce a dual partial cascaded decoder refinement strategy to excavate more informative polyp cues. Extensive experimentations on five widely adopted datasets demonstrate the proposed network outperforms other state-of-the-art on most metrics.

From CAD to representations suitable for isogeometric analysis: a complete pipeline

AbstractNext generation CAD/CAE systems shall integrate design and analysis procedures as a means to simplify workflows, boost performance, and speed up time to market. In the present paper we demonstrate a proof of concept of such a unification using subdivision surfaces and the enabling technology of isogeometric analysis. In particular, we present a complete pipeline to convert CAD models into smooth $$G^1$$ G 1 spline representations, which are suitable for isogeometric analysis. Starting from a CAD boundary representation of a mechanical object, we perform an automatic control cage extraction by means of quadrangular faces, such that its limit Catmull-Clark subdivision surface approximates accurately the input model. We then compute a basis of the $$G^1$$ G 1 spline space over the quad mesh in order to carry out least squares fitting over a point cloud, acquired by sampling the original CAD geometry. The resulting surface is a collection of Bézier patches with $$G^1$$ G 1 regularity. Finally, we use the basis functions to perform isogeometric analysis simulations of realistic PDEs on the reconstructed $$G^1$$ G 1 model. The quality of the construction is demonstrated via several numerical examples performed on a collection of CAD objects presenting various challenging realistic shapes.

Reconstruction method of high-precision longwall mining floor curved surface model driven by data points fitting of equipment

Abstract High-precision coal seam model is the basis of intelligent mining. The longwall mining face floor model, which can provide data sources for the correction of the dynamic coal seam model, is difficult to measurement directly. To address this issue, this paper proposes a high-precision coal seam surface model reconstruction and correction method based on a large number of operating data points. Firstly, the coupling model of equipment and floor is obtained based on the coupling model of plane and surface. Subsequently, the plane data points of the equipment are corrected based on the coupling model of equipment and floor to obtain the floor reconstruction points. The Catmull–Clark surface subdivision method is then used to subdivide the plane formed by the equipment data points to obtain surface subdivision points. The floor model reconstructed using surface data points is validated and corrected using the physics engine in the Unity 3d platform. Finally, the verification of the reconstruction point selection method, the coupling principle between the equipment and the floor model, the reconstruction accuracy of the base plate and the correction principle were carried out based on the equipment and floor model in the laboratory. The experimental results show the feasibility of the floor reconstruction, verification and correction method, which can provide a new idea for the reconstruction of the floor of the longwall mining face and the correction of the dynamic coal seam.

Yielding optimal dielectric energy storage and breakdown properties of lead-free pyrochlore ceramics by grain refinement strategies

In the quest to develop high-performance dielectrics suitable for high-power applications, its microstructure engineering and understanding the effect on the property have become crucial. However, the trade-off relationship between saturated polarization and breakdown strength with respect to the average grain size complicates defining the ideal microstructure for high-performance energy storage dielectrics. In this study, we investigates the impact of the microstructure of (Bi1.5Zn0.5)(Zn0.5Nb1.5)O7 (BZN) pyrochlore ceramics on their energy storage characteristics by adjusting sintering temperatures. Interestingly, dielectric properties, energy storage capabilities, and electrical fatigue characteristics exhibit minimal change, maintaining a high dielectric constant (∼160) and a low dielectric loss (< 10−4). Moreover, BZN ceramics demonstrate quasi-linear dielectric properties up to 250 kV cm−1, with slight fluctuation in energy density as the function of grain size (1.08–1.26 J cm−3). This weak dependence of energy storage on microstructure underscores that the enhancement of BZN ceramics for energy storage devices is more effectively achieved by improving breakdown strength through microstructural refinement rather than grain enlargement, which has limited impact on energy density in BZN systems. The findings from this study offer valuable insights into optimizing the microstructures of multilayer systems that are targeted at augmenting volumetric energy density, laying the groundwork for further advancements in dielectric materials.

Adaptive PF-CZM for multiphysics fracture analysis in functionally graded materials

Functionally graded materials (FGM) hold significant relevance in engineering due to their tailored material property gradation, designed for specific engineering applications. The challenge in fracture analysis of FGM stems from the spatial variation of material properties, which complicates the prediction of crack topology. Local and global refinement strategies are impractical for fracture analysis in FGM due to the unpredictable nature of crack topology, which renders local refinement infeasible. Additionally, global refinement is not advisable as it leads to a significant increase in degrees of freedom, adversely affecting computational efficiency.The novelty of this research lies in the incorporation of spatial variation in both the length scale and material properties, enhancing the realism of FGM domain modeling. To preserve the required length scale, it is necessary to adopt a minimum mesh size, which consequently results in a substantial increase in the degrees of freedom and, thereby, escalates the computational cost. To address these challenges, the study employs an adaptive mesh refinement (AMR) algorithm integrated with a phase-field cohesive zone model (PF-CZM), providing a robust solution for accurate fracture analysis in FGM. Based on the ideas stemming from the need for efficient and realistic modeling, the AMR-PF-CZM framework refines the mesh efficiently in regions of crack growth based on crack-driving energy and phase field variable, thereby eliminating the need for pre-refinement. The findings demonstrate a 76%–85% increase in computational efficiency and accuracy of the AMR-PF-CZM approach compared to the non-adaptive PF-CZM. Furthermore, the developed algorithm’s applicability to dynamic fracture and multi-physics problems, specifically addressing mechanical and thermal fracture in FGM, underscores the importance of this approach in capturing complex fracture phenomena.

Invariant set estimation for piecewise affine dynamical systems using piecewise affine barrier function

This paper introduces an algorithm for estimating the invariant set of closed-loop controlled dynamical systems identified using single-hidden layer Rectified linear units (ReLU) neural networks or piecewise affine (PWA) functions, particularly addressing the challenge of providing safety guarantees for single-hidden layer ReLU networks commonly used in safety–critical applications. The invariant set of PWA dynamical system is estimated using single-hidden layer ReLU networks or its equivalent PWA function. This method entails formulating the barrier function as a PWA function and converting the search process into a linear optimization problem using vertices. We incorporate a domain refinement strategy to increase flexibility in case the optimization does not find a valid barrier function. Moreover, the objective of the optimization is to find a less conservative invariant set based on the current partition. Our experimental results demonstrate the effectiveness and efficiency of our approach, demonstrating its potential for ensuring the safety of PWA dynamical systems.

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

A reliability-based design optimization strategy using quantile surrogates by improved PC-kriging

In recent years, the surrogate-assisted reliability-based design optimization (RBDO) methods have been continuously developed, and numerous advanced optimization strategies have boosted efficiency and accuracy. However, ensuring sufficient accuracy and feasibility at the optimal is still a challenge. In order to achieve a well-balanced between efficiency, accuracy, and optimal feasibility, in this work, a RBDO strategy using quantile surrogates by improved PC-Kriging model is proposed. The novelty of the proposed method lies in the following main aspects: Firstly, an improved learning function has been developed to significantly enhance the convergence efficiency during the construction of the PC-Kriging model. Secondly, in the RBDO analysis process, a novel "MP+EI" combination point addition strategy is adopted to enhance the approximation of the surrogate model to the optimum of the objective function. It can further improve optimization efficiency and accuracy. On the basis of the rough probability constrained surrogate model established by the global enrichment strategy, a local refinement strategy is introduced to guarantee the accuracy of the quantile evaluation of the probability constrained surrogate model for each iteration solution during the optimization process. Finally, the proposed method is validated by three typical RBDO test examples and one engineering application example.

Grain refinement for indium zinc oxide ceramic targets by praseodymium doped induced blocked boundary migration

How to refine the grain has been a difficult problem in the preparation of high-performance ceramic targets. In this work, the doping-induced grain refinement strategy was proposed, indium zinc oxide doped with different concentrations of Pr (Pr-doped IZO, PrIZO) targets were obtained by optimizing the sintering time and holding temperature. Effects of Pr doping content on the density, phase microevolution, grain size and resistivity during the densification process as well as the kinetics of the grain growth and the mechanism of grain refinement of PrIZO targets were investigated in detail. The results demonstrated that PrIZO targets with the atomic ratios of Pr:In:Zn=0.01-0.03:1:1 all exhibited the excellent performance with high densification (>99.10%) and mere average grain size (<3.0 μm) at low sintering temperature of 1350 °C. Additionally, XRD and EDS analysis indicated that PrIZO targets were composed of In2O3 and Zn3In2O6 with slight PrInO3, which formed by the limited solid solubility of Pr element. Combined with theoretical calculations, it inferred that the mechanism of grain refinement was attributed to the solute transformation and fine PrInO3 distributed at the grain boundaries of In2O3 phases, which produced the drag effect of grain boundary migration, then hindering the further growth of grains.

Analyzing the Impact of the “Double Reduction” Policy on Student Well-Being and Academic Performance in Urban China

The “Double Reduction” policy, introduced in China in 2021, seeks to alleviate academic pressure on students by reducing homework loads and regulating private tutoring services. This study analyzes the policy’s impact on student well-being and academic performance in urban settings, drawing on quantitative and qualitative data from students, parents, teachers, and school administrators. The findings indicate a dual impact: a decrease in stress and anxiety levels and improvements in social behaviors and time management, but also challenges in maintaining academic discipline and performance, particularly among students preparing for high-stakes exams. While the policy promotes a more balanced approach to education, it requires further refinement and adaptive strategies to address diverse student needs effectively. The study concludes that ongoing support and monitoring are essential to achieve the policy’s long-term objectives of reducing stress while ensuring academic excellence.

An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

Mixed-mode thermo-mechanical fracture: An adaptive multi-patch isogeometric phase-field cohesive zone model

This work presents an adaptive phase-field cohesive zone model (PF-CZM) for simulating mixed-mode crack nucleation and growth in isotropic rock-like materials subjected to thermo-mechanical interactions. The proposed approach combines an adaptive multi-patch isogeometric analysis (MP-IGA) and length-scale insensitive PF-CZM. The formulation captures the distinct critical energy release rates for Mode-I and Mode-II fractures, which is crucial for predicting mixed-mode thermo-mechanical fracture behavior in isotropic rock-like materials. The PF-CZM governing equations are solved with isogeometric analysis based on locally refined non-uniform rational B-splines (LR NURBS), and the complex structural geometry is exactly described with multiple LR NURBS patches. The field variables, such as displacement, phase-field, and temperature at the interface of adjacent patches, are coupled using Nitsche’s method. To enhance the computational efficiency while maintaining accuracy, a refinement-correction adaptive scheme combined with the structured mesh refinement strategy is developed. The proposed framework is validated against recent numerical and experimental results in the literature, particularly in the context of capturing complex behavior of mixed-mode crack propagation in isotropic rock-like materials subjected to thermo-mechanical loading.

An advanced numerical technique for subdivision depth of non-stationary quaternary refinement scheme for curves and surfaces

An advanced numerical technique for subdivision depth of non-stationary quaternary refinement scheme for curves and surfaces