Irregular Geometry Research Articles

Least-Squares Kirchhoff Depth Migration With Fast Point-Spread-Function Computation

Seismic migrations are generally formulated as the adjoint operators of linear forward modeling and often lead to images with degraded resolution, unbalanced illumination and migration artifacts, especially in surveys with geologic complexity and irregular acquisition geometry. Least-squares migration (LSM) is able to mitigate these problems and produce better resolved images that are suitable for subsequent AVO/AVA inversion. However, no matter what domain LSM is implemented in, the computational cost is still several times or even one order of magnitude more than that of traditional migration. In this paper, we present an efficient image-domain least-square Kirchhoff depth migration (LSKDM), in which the Hessian matrix is approximated by a grid of point-spread-functions (PSFs). Traditional PSF computing algorithm requires a non-negligible cost caused by a successive operation of modeling and migration, and has to satisfy a sampling restriction to avoid interference between nearby PSFs. We present in this paper that, by using the ray-based Green’s functions and the linear traveltime approximation, the PSFs can be constructed explicitly at a significantly reduced computational cost and is able to adapt flexible spatial sampling that is fine enough to detect small-scale illumination variation. With the constructed PSFs, we formulate an image-domain LSKDM to iteratively solve for the optimal reflectivities. Numerical tests on synthetic and field data examples demonstrate that the proposed LSKDM is highly efficient and is capable of producing images with enhanced spatial resolution and amplitude fidelity when compared with the Kirchhoff depth migration (KDM) image.

Rapid method for computing the mechanical resonances of irregular objects.

A solid object's geometry, density, and elastic moduli completely determine its spectrum of normal modes. Solving the inverse problem-determining a material's elastic moduli given a set of resonance frequencies and sample geometry-relies on the ability to compute resonance spectra accurately and efficiently. Established methods for calculating these spectra are either fast but limited to simple geometries, or are applicable to arbitrarily shaped samples at the cost of being prohibitively slow. Here, we describe a method to rapidly compute the normal modes of irregularly shaped objects using entirely open-source software. Our method's accuracy compares favorably with existing methods for simple geometries and shows a significant improvement in speed over existing methods for irregular geometries.

Characterization of Potential Cellulose from Hylocereus Polyrhizus (Dragon Fruit) peel: A Study on Physicochemical and Thermal Properties

The strict environmental regulations to overcome the drawbacks of consumption and disposal of non-renewable synthetic materials have motivated this investigation. The physical, chemical, morphological, and thermal properties of Hylocereus Polyrhizus peel (HPP) powder obtained from the raw materials were examined in this study. The physical properties analyzes of Hylocereus Polyrhizus peel (HPP) powder discovered that the moisture content, density, and water holding capacity were 9.70%, 0.45 g/cm3 , and 98.60%, respectively. Meanwhile, the chemical composition analysis of Hylocereus Polyrhizus peel (HPP) powder revealed that the powder was significantly high in cellulose contents (34.35%) from other bio-peel wastes. The crystallinity index of Hylocereus Polyrhizus peel (HPP) powder was 32.76%, according to further X-ray diffraction (XRD) analysis. The thermal stability of Hylocereus Polyrhizus peel (HPP) powder was examined using thermogravimetric analysis (TGA) and found thermally stable at 204°C. The morphological study via scanning electron microscopy (SEM) showed a shriveled and irregular geometry surface. Hylocereus Polyrhizus peel (HPP) powder demonstrated the peak in the range representing the major functional groups responsible for pectin’s properties. Thus, the findings revealed that the Hylocereus Polyrhizus peel (HPP) powder has the potential for the development of biodegradable and renewable materials.

INVESTIGATING THE EFFECT OF LAYERING AND SCHISTOSITY ON THE MECHANICAL BEHAVIOR OF ROCKS USING THE DISCRETE ELEMENT METHOD

Anisotropy and deformation in rock material is mainly caused by the non-uniformity and irregular geometry of fracture systems. This factor is among the important problems in designing and evaluating the stability of engineering structures. In this research, the mechanical behavior of layered and schistose rocks is investigated by conducting compressive uniaxial and triaxial strength laboratory tests on rock samples from different directions and angles varying with respect to the loading axis. The tests were carried out on intact blocks of quartz schist rock with 0° layering and invisible schistosity. Next, numerical modelling was performed using the discrete element method (DEM) and calibrating numerical models by laboratory tests. In this process, three types of connections between the minerals (i.e. mica-mica, mica-quartz, and quartz-quartz), the growth of cracks, and the fracture mechanism of layered rocks with different layering angles were investigated. The results showed that unlike the arrangement of the rock particles, the layering angle of quartz schist has an important effect on the mechanical properties of the rock in such a way that uniaxial compressive strength, Young’s modulus, cohesion, and internal friction angle respectively have the greatest effect due to the change of the layering angle, while the tensile strength has the least effect.

Biodegradable BBG/PCL composite scaffolds fabricated by selective laser sintering for directed regeneration of critical-sized bone defects

The repair of critical-sized bone defects (CSBD) remains a significant challenge in clinical practice. Personalized 3D porous scaffolding is generally considered an ideal approach for directed regeneration of CSBD, which usually has irregular and complex 3D geometry. In this study, we designed a customized porous scaffold rabbit radius based on a body-centered cubic unit. Borate bioactive glass/polycaprolactone (BBG/PCL) composite scaffolds with different BBG contents (0%, 5%, 10%, 20%, and 40%) were fabricated using selective laser sintering. The pore geometry, porosity, mechanical strength, hydrophilicity, protein absorption, degradation behavior, in vitro cytocompatibility, and osteogenesis differentiation of the composite scaffolds were systematically characterized. The in vivo biological properties of the BBG/PCL composite scaffold for CSBD repair were assessed using a rabbit foreleg radius defect model. The results indicate that the BBG/PCL composite scaffold with 20% BBG content effectively promoted the proliferation of osteoblasts and repaired the rabbit radius defects. The mechanical strength of the scaffold was sufficient to maintain the integrity of the scaffold structure during a relatively long CSBD repair. Our results indicate that the BBG/PCL composite scaffold can be anticipated to be a promising biomaterial for CSBD repair.

Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation

Numerical simulations for spatiotemporal processes involving material, geometrical and contact nonlinearities might be computationally prohibitive for many-evaluation applications. Considering the strong nonlinearity of dynamic processes, the prediction of high-dimensional spatiotemporal responses remains challenging for most surrogate-based models. To address such issues, a Stochastic Dependency Neural Estimator (SDNE) is proposed to construct the mapping from the input parameter domain to the high-dimensional spatiotemporal response domain, revealing the underlying relationship between the high-dimensional regression estimation and manifold distribution hypothesis. Moreover, Graph Neural Networks (GNN) and Temporal Convolutional Neural Networks (TCN) are integrated to capture nonlinear features from irregular geometries and time series simultaneously. Two high-speed impact cases involving large deformation and highly nonlinear transients are investigated to validate the proposed method. The proposed SDNE achieves highly accurate prediction compared with Finite Element (FE) evaluations. To further validate the practical application of the proposed method, multi-objective crashworthiness optimizations are performed with the proposed SDNE. The results show that the proposed SDNE provides reliable predictions for spatiotemporal dynamics

An efficient hybrid mesh method for seismic traveltime computation with irregular surfaces

Accurate and efficient traveltime computation with irregular surfaces is a prerequisite for traveltime tomography, seismic location, and seismic exploration in mountainous areas. The eikonal equation method is computationally favourable and has gained increasing popularity for obtaining traveltime in recent years. Nowadays most algorithms that can solve eikonal equation with irregular surfaces are based on fully unstructured triangular mesh or surface-fitting mesh. However, these methods have poor performance in terms of computational cost and accuracy. To cope with this problem, we develop a hybrid mesh method based on fast-sweeping-based eikonal solver with rectangular and unstructured triangular mesh for obtaining traveltime with irregular surfaces. In other words, we propose to divide the model into several different regions, which are discretized by rectangular mesh and unstructured triangular mesh respectively. Near the irregular surfaces of model, we use fast sweeping method based on unstructured triangular mesh which releases the full potential of unstructured triangular mesh’s flexibility in accurately describing the irregular geometry of computation domain. For the rest part of the model, we adopt rectangular mesh to make full use of high efficiency of the corresponding high-order fast sweeping algorithm. At the interface of two types of mesh, we need interpolate the known nodes’s traveltimes to get the boundary condition of another mesh, so as to start the fast sweeping method therein. It is found that the accuracy of hybrid mesh method can be increased by 83%, the computation time is reduced by 72%, and the storage requirement is reduced by 72%, compared with the fast sweeping method with full triangular mesh, when the rectangular mesh proportion is 80% in our model. Therefore, this proposed hybrid mesh method can greatly enhance the computational efficacy and serve as an potential forward engine in seismic inversions.

Proposal of point-wise power reconstruction for IRT-4M fuel assemblies in coupled Monte-Carlo calculations

Current efforts to develop high-fidelity multi-physics tools for research reactors with IRT-4M fuel leads to the involvement of Monte-Carlo transport and subchannel thermo-hydraulic codes in the process of safety assessment. Monte-Carlo based neutronic codes are often used as a reference tool and are suitable for a prediction of a detailed power distribution. This paper proposes a method of the point-wise power reconstruction based on a Radial Basis Function (RBF) interpolation for complicated irregular geometries and compares different detector functionalities of the Serpent2 code from the time efficiency point of view. Furthermore, a preliminary validation of the applied method for IRT-4M in a subchannel code SUBSALS mesh is presented. This code is currently under the evelopment at the Research Centre Rež and the Czech Technical University in Prague and focuses on the thermohydraulic analysis of IRT type fuel assemblies.

Modeling and experimental analysis of surface topography generation mechanism during ultrasonic vibration-assisted grinding

Surface morphology is an important index to evaluate the grinding performance of grinding wheel, and abrasive grains and grinding fluid have a great influence on the formation of grinding surface. The abrasive grains used in actual grinding are irregular geometries, and the distribution on the grinding wheel is also irregular, which brings difficulties to the study of the surface topography. Based on the actual geometric shape of CBN abrasive grains, the corresponding geometric features are extracted and models of CBN abrasive grains are established in this paper. Under the premise of considering the randomness of the abrasive grains distribution and the material removal mechanism, the simulation of the grinding surface was carried out to predict the 3D surface topography. Through the established experimental platform, the related experiments of dry grinding and wet grinding of common grinding (CG) and ultrasonic vibration assisted grinding (UVAG) were carried out. The experimental results are in good agreement with the simulation results. In dry grinding experiments, tangential ultrasonic vibration-assisted grinding (TUVAG) produces groove-like ultrasonic vibration textures that facilitate material removal. Radial ultrasonic vibration-assisted grinding (RUVAG) has a large impact on the machined surface, and pits and a large number of micro-cracks appear, which effectively weakens the strength of the material. In wet grinding experiments, the grinding fluid effectively improves the working conditions of the grinding zone and obtains better surface quality. The surface roughness of TUVAG is the lowest under both dry grinding and wet grinding conditions. Under the condition of dry grinding, the surface roughness of RUVAG is better than that of CG. The opposite is true in wet grinding.

In situ study on heavy ion irradiation induced microstructure evolution in single crystal Cu with nanovoids at elevated temperature

Understanding the response of nanovoids to irradiation damage advances our capability to design radiation tolerant materials. Recent in situ studies reveal the shrinkage of hexagon-shaped nanovoids in (110) textured Cu. However, the evolution of voids with irregular geometries under irradiation is less well understood. Here, in situ Kr ion irradiation was performed at 100 °C on single-crystal Cu (112) that possesses nanovoids with high aspect ratio. In situ studies show these elongated voids fragment into smaller voids and shrink gradually with increasing dose. Phase field simulations confirm that fragmentation of the void is governed by the competing kinetics between atoms diffusing toward and away from the elongated nanovoid. Post irradiation analysis reveals the formation of high-density defect clusters.

Curl-Flow

We propose to augment standard grid-based fluid solvers with pointwise divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level. Our method takes as input a discretely divergence-free velocity field generated by a staggered grid pressure projection, and first recovers a corresponding discrete vector potential. Instead of solving a costly vector Poisson problem for the potential, we develop a fast parallel sweeping strategy to find a candidate potential and apply a gauge transformation to enforce the Coulomb gauge condition and thereby make it numerically smooth. Interpolating this discrete potential generates a point-wise vector potential whose analytical curl is a pointwise incompressible velocity field. Our method further supports irregular solid geometry through the use of level set-based cut-cells and a novel Curl-Noise-inspired potential ramping procedure that simultaneously offers strictly non-penetrating velocities and incompressibility. Experimental comparisons demonstrate that the vector potential reconstruction procedure at the heart of our approach is consistently faster than prior such reconstruction schemes, especially those that solve vector Poisson problems. Moreover, in exchange for its modest extra cost, our overall Curl-Flow framework produces significantly improved particle trajectories that closely respect irregular obstacles, do not suffer from spurious sources or sinks, and yield superior particle distributions over time.

The design and implementation of distributed architecture in the CMOR motion control system

The CMOR Motion Control System is a multi-degree-of-freedom, long-beam, heavy-duty robot system with end effectors used to maintain the blanket and divertor of the China Fusion Engineering Test Reactor (CFETR) with irregular structural geometry. Since the multi-degree of freedom, large size, and flexible structure of the fusion robot will increase the complexity of the algorithm, it is necessary to design the corresponding control system and auxiliary algorithm to realize the motion control of the robot. In this paper, a feasible CMOR (CFETR Multipurpose Overload Robot) motion control system architecture is proposed, which adopts the distributed control structure of the upper and lower machine. Firstly, the game theory algorithm decouples the macro arm with 9-degree-freedom and the micro arm with 14 degree-of-freedom (dual 7-DOFs robots) and separates the pose and accuracy targets after planning the motion of the target task with the Markov chain. Secondly, to improve the accuracy, the kinematics controller based on macro arm motion planning strategy for initial positioning and the force-position hybrid controller based on micro arm motion planning strategy for compensation for precise positioning are designed separately. Finally, a robot distributed architecture is designed to realize offline and online simulation and control in which the offline mode simulates a collision-free trajectory based on the game theory and the online mode synchronously displays fusion robot motion by digital twin technology. The feasible CMOR motion control system architecture is proposed in this paper which can assist complex algorithms to achieve intelligent control in the fusion application.

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

Magneto-hydrothermal triple-convection in a W-shaped porous cavity containing oxytactic bacteria

Bioconvective heat and mass transport phenomena have recently been the subject of interest in diverse fields of applications pertaining to the motion of fluids and their thermophysical properties. The transport processes in a system involving triple convective phenomena, irregular geometry, and boundary conditions constitute a complex phenomenon. This work aims to explore the mixed thermo-bioconvection of magnetically susceptible fluid containing copper nanoparticles and oxytactic bacteria in a novel W-shaped porous cavity. The buoyant convention is generated due to the isothermal heating at the wavy bottom wall, whereas the mixed convection is induced due to the shearing motion of the top-cooled sliding wall. Furthermore, the bioconvection is induced due to the manifestation of oxytactic bacteria or organisms. The inclined sidewalls are insulated. The geometry is packed with water based Cu nanoparticle mixed porous structure, which is subjected to a magnetizing field acted horizontally. The complex transport equations are transformed into nondimensional forms, which are then computed using the finite volume-based developed code. The coupled triple-convective flow physics are explored for a wide range of involved controlling parameters, which could provide helpful insight to the system designer for its proper operation. The shape of geometry can be considered one of the important parameters to control the heat and mass transport phenomena. In general, the influence of amplitude (δ) is more compared to the waviness number (m) of the undulations. The magnitude of heat (Nu) and mass (Sh) transfer rate for the W-shaped cavity is high compared to conventional square and trapezoidal-shaped cavities. The output of the analysis could be very helpful for the designer for modeling devices operating on nanotechnology-based bioconvection, microbial fuel cells, and others.

A strong-form meshfree computational method for plane elastostatic equations of anisotropic functionally graded materials via multiple-scale Pascal polynomials

A strong-form meshfree method is proposed for solving plane elastostatic equations of anisotropic functionally graded materials. Any general function may be the grading function and it is changing smoothly from location to location in the material. The proposed method is based on Pascal polynomial basis and multiple-scale technique and it is a genuinely meshfree method since no numerical integrations over domains and meshing processes are required for considered problems. Implementation of the proposed method is straightforward and the method gives very accurate results. Stability of the solutions are examined numerically in occurrence of random noise. Some certain test problems with known exact solutions are solved both on regular and irregular geometries. Acquired solutions by the suggested method are compared with the exact solutions as well as with solutions of some existing numerical techniques in literature, such as boundary element, meshless local Petrov–Galerkin and radial basis function based meshless methods, to show accuracy of the proposed method.

New Advances of the Multiscale Approach for the Analyses of InSAR Ground Measurements: The Yellowstone Caldera Case-Study

In this study, we describe new advances in the multiscale methodology to allow a more realistic interpretation of volcanic deformation fields by investigating geometrically irregular bodies and multi-source scenarios. We propose an integrated approach to be applied to InSAR measurements, employing the Multiridge and ScalFun methods and the Total Horizontal Derivative (THD) technique: this strategy provides unconstrained information on the source geometrical parameters, such as the depth, position, shape, and horizontal extent. To do this, we start from conditions where the biharmonic deformation field satisfies Laplace’s equation and homogeneity law. We test the use of the multiscale procedures to model single and multisource scenarios with irregular geometries by retrieving satisfactory results for a set of simulated sources. Finally, we employ the proposed approach to the 2004–2009 uplift episode at the Yellowstone Caldera (U.S.) measured by ENVISAT InSAR to provide information about the volcanic plumbing system. Our results indicate a single ~50×20 km2 extended source lying beneath the caldera at around 10 km b.s.l. (depth to the center), which is shallower below both the resurgent domes (6–7 km b.s.l. depth to the top).

A robust migration velocity analysis method based on adaptive differential semblance optimization

Migration velocity analysis (MVA) is an efficient tool to reconstruct low wavenumber components of the model. Compared with data domain methods, the MVA methods are implemented in image domain by processing imaging results directly through minimizing moveouts and improving the coherence of the common image gathers (CIGs), such as angle domain common image gathers (ADCIGs) or subsurface offset domain image gathers (ODCIGs). As one of the wave-equation based migration velocity analysis (WEMVA) methods, differential semblance optimization (DSO) can automatically detect the moveout existed in the CIGs and is convenient to implement. However, referring to the CIGs contaminated by spurious imaging artefacts caused by uneven illumination and irregular observation geometry, the traditional DSO method may produce poor velocity update with oscillations, which can prevent rapid convergence to a correct velocity model. To deal with this issue and extend the solution space, we introduce an adaptive DSO (ADSO) method by replacing the traditional image matching with energy distribution matching. By reducing dependence on the imaging results and introducing a variable weighting function in the calculation of the adjoint sources, the amplitude of the inverted model can be evenly distributed and free of artefacts. Three numerical examples show that the ADSO method is robust with little artefacts presenting in the gradients. Combined with the improved gradient, the ADSO can lead to a stable update.

Antimicrobial Shape Memory Polymer Hydrogels for Chronic Wound Dressings.

Chronic wounds can remain open for several months and have high risks of amputation due to infection. Dressing materials to treat chronic wounds should be conformable for irregular wound geometries, maintain a moist wound bed, and reduce infection risks. To that end, we developed cytocompatible shape memory polyurethane-based poly(ethylene glycol) (PEG) hydrogels that allow facile delivery to the wound site. Plant-based phenolic acids were physically incorporated onto the hydrogel scaffolds to provide antimicrobial properties. These materials were tested to confirm their shape memory properties, cytocompatibility, and antibacterial properties. The incorporation of phenolic acids provides a new mechanism for tuning intermolecular bonding in the hydrogels and corollary mechanical and shape memory properties. Phenolic acid-containing hydrogels demonstrated an increased shape recovery ratio (1.35× higher than the control formulation), and materials with cytocompatibility >90% were identified. Antimicrobial properties were retained over 20 days in hydrogels with higher phenolic acid content. Phenolic acid retention and antimicrobial efficacy were dependent upon phenolic acid structures and interactions with the polymer backbone. This novel hydrogel system provides a platform for future development as a chronic wound dressing material that is easy to implant and reduces infection risks.

A Reconfigurable Parallel Robot for On-Structure Machining of Large Structures

This paper presents a novel walking hybrid-kinematics robot that can be reconfigured to have three, five, and six degrees of freedom (DOFs) for adsorption machining of large structures. A symmetric 3PRPR or 3PRRR parallel mechanism with three translational (3T) DOFs is used to perform three-axis machining tasks. Three attachment pads connected to passive spherical joints are used to attach the robot to the surface of a large structure. Two or three rotational degrees of freedom can be added to the robot to adapt to a large structure’s irregular surface geometry and perform five- or six-axis machining tasks. This is achieved through modular reassembly or joint locking that reconfigures the robot from a three-DOF robot to a five- or six-DOF robot. A serial module providing two rotational DOFs can be added to the 3T parallel mechanism to provide five DOFs. A parallel module, namely 3SPR or 3SU mechanism, can be added to the 3T parallel mechanism to provide six DOFs. The mobility, pose kinematics, differential kinematics, singularities, and workspace of the 3SPR and 3SU parallel mechanisms alone and combined with the 3T mechanism are discussed in this paper. It is shown that the singularities of the mechanism can be easily avoided by making the moving platform of the 3SPR or 3SU mechanism smaller than the base, limiting the range of some joints, and having an appropriate length of the links. Furthermore, a method to optimize the workspace of the mechanism was also discussed.

Analysis of variance dimension of reinforcement to stress concentration factor using Finite Element Method

Stress concentration is a condition when stress increases only at a certain area of a component compared to the stress at the uniform cross-section of the component when given a load. This phenomenon can occur due to irregular geometry such as a hole, sharp angle, cross-section change, notch, threads, groove, crack, etc. Any change in geometric shape on a uniform cross section will cause a stress increases. High stress concentrations need to be reduced to prevent faster failure of a component. The stress concentration that occurs can be determined by using stress concentration factors (Kt). Using FEM method, the simulation obtained stress distribution that occurs in the shaft that is modelled, both without and with the additional groove. The results obtained by the additional of a semi-circular groove can reduce stress concentrations up to ten percent.