Geometry Data Research Articles

Smart Autodriver Algorithm for Real-Time Autonomous Vehicle Trajectory Control

Autodriver algorithm aims to develop a path-following algorithm for autonomous vehicles using road geometry data and vehicle dynamics. In this study, a novel smart Autodriver algorithm is developed according to practical implications utilizing a more realistic vehicle model and consideration of real-time applicability. A ghost-car path-following approach is introduced to define the desired location of the vehicle at every instance during various maneuvers. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses are discussed and used to construct a path-following controller based on the Autodriver algorithm. A feedback control based on Sliding Mode Control (SMC) is also designed and applied to minimize transient errors between the road and the vehicle positions. Finally, simulations are performed to analyze the path-following performance of the proposed scheme compared to a Model Predictive Controller (MPC) as a widely accepted popular method for autonomous vehicles. Hardware-in-the-loop (HIL) tests are also performed to investigate real-time applicability of the controllers. The results show promising controller performance in terms of error minimization, passenger comfort, and low computational cost for the proposed method.

Isogeometric static analysis of laminated plates with curvilinear fibers based on Refined Zigzag Theory

In this study, we propose a new IsoGeometric formulation based on Refined Zigzag Theory (RZT), abbreviated as IG-RZT, for static analysis of laminated plates and sandwich panels with curvilinear fiber paths for the first time in literature. The original RZT formulation defines constant, linear, and zigzag deformation contributions of thickness coordinate to the in-plane displacements. This kinematic relation can accurately predict in-plane displacement of composite with straight fibers. However, estimating a realistic variation of in-plane displacements in a variable angle tow (VAT) composite is a more challenging problem as compared to the straight fiber composites. This difficulty has been addressed herein by including a quartic (fourth-order) polynomial thickness expansion that includes the transverse normal strain effects to the kinematic displacement fields of RZT. Moreover, the modelling of VAT composites results in the RZT zigzag functions to depend not only on the thickness coordinate but also on the in-plane positions. The present IG-RZT methodology is free of shear-locking and shear correction factors due to the integration of RZT with Non-Uniform Rational B-Splines (NURBS) functions of isogeometric analysis. The use of NURBS functions also enable the exact geometry data to be taken directly from a computer aided design (CAD) software, e.g., Rhinoceros, into an in-house Mathematica code. The accuracy and efficiency of the present IG-RZT formulation is assessed and validated by solving various examples of curvilinear fiber laminated plates with different aspect and span-to-thickness ratios. Comparison of IG-RZT results with reference solutions available in literature and generated by a commercial software (ANSYS) using 3D finite elements have demonstrated the remarkable benefits of the proposed IG-RZT method for predicting highly accurate both displacement and stress distributions of VAT composite structures.

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

Design and characterization of 3D multilayer woven reinforcements shape memory polymer composites

Shape memory polymer (SMP) composites are attractive and excellent smart materials due to their outstanding properties and rich functionality as they combine typical mechanical and functional properties of composites with shape memory properties. In particular, 3D reinforced preforms have tremendous potential for the development of functional composites by using the capabilities of 3D woven fabric preform design, and polymer shape memory behavior. Within that scope, this work aims to investigate the shape memory behavior and shape recovery properties of a specific type of 3D multilayer woven SMP composite in response to external stimuli. For this purpose, nine different multilayer stitched fabrics are produced with different weave structures, and different fabric thread densities using polyimide filaments. Then, a series of tests is carried out on these fabrics to evaluate their mechanical and physical properties. The layered fabric design that delivers high mechanical performance is next involved to manufacture the SMP composite samples, for which shape recovery capability is investigated. Fold-deploy and other shape memory cycle tests are performed to evaluate the shape memory characteristics. An optical 3D scanner based on fringe projection is further proposed to precisely acquire the geometry data and perform deformation analysis to quantitatively evaluate the shape fixity and shape recovery behaviors. The results from this study are very promising, demonstrating that these multilayer SMP structures can successfully be recovered following the desired design constraints without noticeable damage.

Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data

SUMMARYWe propose an advanced version of non-linear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic data set with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50–75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.

Flow simulation and performance analysis of a drilling turbine

Turbodrills are axial hydraulic turbines which are used in drilling hydrocarbons in extreme conditions, possessing advantages over other drilling techniques with their high speed of rotation, and higher operating torques.In The present work, a numerical simulation of a Newtonian Drilling Mud flow was carried out through one stage of this turbine that has a stator and a rotor. The steady state mixing plane model was used for the simulations to take the rotation of the rotor into account besides the spatially averaged property fluxes of the flow. The turbulence K-e model was used to consider the turbulence effects.Key performance parameters are calculated as a function of rotation speed and they are validated against the experimental data of the same model geometry in real operating conditions, a good agreement have been found between manufacturer power and torque data, and our simulation results for the same variables.Various flow fields are presented such as velocity and pressure, which had a great influence on the performance of the studied turbine. This will lead to choose the best parameters configuration of an optimal field operation.

Extended tight‐binding quantum chemistry methods

AbstractThis review covers a family of atomistic, mostly quantum chemistry (QC) based semiempirical methods for the fast and reasonably accurate description of large molecules in gas and condensed phase. The theory is derived from a density functional (DFT) perturbation expansion of the electron density in fluctuation terms to various orders similar to the original density functional tight binding model. The term “eXtended” in their name (xTB) emphasizes the parameter availability for almost the entire periodic table of elements (Z ≤ 86) and improvements of the underlying theory regarding, for example, the atomic orbital basis set, the level of multipole approximation and the treatment of the important electrostatic and dispersion interactions. A common feature of most members is their consistent parameterization on accurate gas phase theoretical reference data for geometries, vibrational frequencies and noncovalent interactions, which are the primary properties of interest in typical applications to systems composed of up to a few thousand atoms. Further specialized versions were developed for the description of electronic spectra and corresponding response properties. Besides a provided common theoretical background with some important implementation details in the efficient and free xtb program, various benchmarks for structural and thermochemical properties including (transition‐)metal systems are discussed. The review is completed by recent extensions of the model to the force‐field (FF) level as well as its application to solids under periodic boundary conditions. The general applicability together with the excellent cost‐accuracy ratio and the high robustness make the xTB family of methods very attractive for various fields of computer‐aided chemical research.This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Electronic Structure Theory > Semiempirical Electronic Structure Methods Software > Quantum Chemistry

Elegant procedure to estimate series capacitance of a uniform transformer winding from its measured FRA: implementable on existing FRA instruments

A simple procedure to estimate series capacitance of a uniform transformer winding from its measured frequency response analysis (FRA) and shunt capacitance is presented. Unlike previously published approaches, this method does not involve any cumbersome and time-consuming curve-fitting nor running optimisation/search algorithms, and neither does it require data of winding geometry. The procedure relies on a property that is observable in the impedance function of a lossless winding, viz., the ratio of the product of squares of open circuit natural frequencies to the product of squares of short circuit natural frequencies bears a special relation to impedance function coefficients. Its feasibility was initially verified by simulation, and then by experiments on small-sized continuous-disk and interleaved-disc windings, followed by a large-sized 33 kV, 3.5 MVA continuous-disc winding, and finally on a 315 kVA 11/13.8 kV transformer. After measuring FRA, the process involves just finding roots of a polynomial, from which the initial impulse voltage distribution constant and series capacitance can directly be determined. Given these attractive features, authors believe that this method is implementable on existing FRA instruments, so that, along with routinely measured FRA, these two important constants of a winding can be displayed.

Analysis of the Effect of Urban Geometric Form Factors on Heat Islands in Guangzhou

The geometric form factors of urban space include height width ratio (H/W), sky view factor (SVF) and street direction, which directly affect the incidence of solar radiation, and then affect the temperature change of the block. In this study, the representative hot island and cold island streets in Guangzhou are selected as the research objects. According to the intensity and range distribution map of Guangzhou heat island in summer of 2014-2017 issued by Guangzhou Meteorological Bureau, the intersection area with high intensity of 4-year continuous heat island is found out by two-dimensional image superposition. In the region, the typical measurement points and comparison points are selected in groups, the temperature of each measurement point is monitored, and the urban geometry data is collected for comparative analysis. The results show that under the condition of the same underlying surface factor and no significant difference in artificial heat factor, the urban geometry can alleviate the heat island effect, and the measuring points with smaller sky view factor and larger height width ratio (H/W) have lower temperature, and the temperature of each measuring point has higher correlation with the size of SVF. In addition, the direction defined by the long axis of the valley will also affect the solar radiation equivalent of the street pavement, and the reclamation open land is also easy to become a heat island area.

Numerical Simulation on Heat Transfer Characteristics of Water Flowing through the Fracture of High-Temperature Rock

Deep geothermal resources are becoming an increasingly important energy source worldwide. To achieve the optimal efficiency of this resource, the heat transfer characteristics between flowing water and rock need to be further studied. Using the stereotopometric scanning system 3D CaMega, the fracture geometry data of five cuboid granite rocks were obtained to determine the effects of fracture roughness on the heat transferability of rock. A 3-D model was built based upon the scanned geometry data to assess the effects of rock temperature, water velocity, and roughness, and aperture size of fracture surface on the heat transfer coefficient. The simulation tests show that water velocity has the most noticeable effect, followed by aperture size and rock roughness. On the other hand, the initial rock temperature has the least influence. A new heat transfer coefficient was proposed considering aperture size, water flow velocity, and rock fracture roughness. The calculated values of Reynolds, Prandtl, and Nusselt numbers obtained using this coefficient are in good agreement with the numerical simulation results. This study provides a reference for enhancing the heat transfer coefficient to benefit the exploitation of heat energy of hot dry rock.

Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient-specific data assimilation.

Coronary artery bypass grafts (CABG) surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease. In this work, we apply a numerical optimal flow control model to patient-specific geometries of CABG, reconstructed from clinical images of real-life surgical cases, in parameterized settings. The aim of these applications is to match known physiological data with numerical hemodynamics corresponding to different scenarios, arisen by tuning some parameters. Such applications are an initial step toward matching patient-specific physiological data in patient-specific vascular geometries as best as possible. Two critical challenges that reportedly arise in such problems are: (a) lack of robust quantification of meaningful boundary conditions required to match known data as best as possible and (b) high computational cost. In this work, we utilize unknown control variables in the optimal flow control problems to take care of the first challenge. Moreover, to address the second challenge, we propose a time-efficient and reliable computational environment for such parameterized problems by projecting them onto a low-dimensional solution manifold through proper orthogonal decomposition-Galerkin.

Modeling crash risk of horizontal curves using large-scale auto-extracted roadway geometry data

Highway horizontal curves (H-curves) provide a smooth transition between two tangent sections of roadways. They allow vehicles to adjust their travel directions gradually. However, the geometry changes of the highway sections with H-curves often raise safety concerns. In order to deploy effective safety countermeasures, a critical task is to understand the risk factors associated with H-curves. Existing studies have made efforts to probe the safety issues associated with H-curves, whereas they were limited to relatively small-scale examinations because of the challenges in identifying H-curves from large road networks. In addition, due to the lack of well-archived traffic and roadway information, gathering other data associated with the H-curves is also difficult. Regarding to these gaps, this study aims to leverage open-source data to analyze the crash risk of highway sections with H-curves. In particular, the present study highlights itself from two main aspects: (i) a H-curve extraction tool was developed to facilitate large-scale curve data collection through the analytics of different open source data; and (ii) a crash modeling framework was developed to quantify H-curve crash risk. A case study based on a statewide road network was performed to test the developed crash risk models with the collected curve data. The results show the opportunities of using the developed tool for large-scale data collection and analyze the safety impacts of H-curve geometric properties, elevation change, traffic exposure, among others. Findings of this study provide insights into the improvement of H-curve data collection and safety evaluation.

Automated simulation of voxel-based microstructures based on enhanced finite cell approach

A new and efficient method is proposed for the decomposition of finite elements into finite subcells, which are used to obtain an integration scheme allowing to analyse complex microstructure morphologies in regular finite element discretizations. Since the geometry data of reconstructed microstructures are often given as voxel data, it is reasonable to exploit the special properties of the given data when constructing the subcells, i.e. the perpendicularly cornered shape of the constituent interfaces at the microscale. Thus, in order to obtain a more efficient integration scheme, the proposed method aims to construct a significantly reduced number of subcells by aggregating as much voxels as possible to larger cuboids. The resulting methods are analysed and compared with the conventional Octree algorithm. Eventually, the proposed optimal decomposition method is used for a virtual tension test on a reconstructed three-dimensional microstructure of a dual-phase steel, which is afterwards compared to real experimental data.

Rock Mass Structural Characterization Through DFN–LiDAR–DOS Methodology

Underground projects require a realistic understanding of geological, structural and mechanical characteristics of the rock mass. This includes a comprehensive rock mass characterization, assessment to all existing site information and an accurate geological model. However, due to the lack of truly three-dimensional subsurface fracture geometry data, this reality is rendered difficult. Thus, the use of Discrete Fracture Networks (DFNs) arises as an interesting tool. DFN models are statistically generated from discontinuity features mapped in situ. These deterministic data can be obtained by employing various techniques, and more specifically with the use of laser scanning (i.e. Light Detection and Ranging—LiDAR). Complimentary discontinuity data can be obtained through fiber optic sensors coupled to tendon rock support elements, adding structural information from within the rock mass to the DFN model. This paper presents the methodology associated with the implementation of LiDAR data into DFN models for the purpose of application in underground projects and the use of optical sensors to capture three-dimensional loading of tendon support and, in turn, identify intersecting discontinuity planes. The Distributed Optical Strain Sensing technology is demonstrated through double shear laboratory tests, as well as through an in situ application to monitor rock mass movement during a tunnel excavation.

Scale-Resolving Simulations of a Civil Aircraft Wing Transonic Shock-Buffet Experiment

The flow physics governing the shock-buffet phenomenon on swept wings remain disputed without an unequivocal description. Herein is a contribution to the ongoing discussion that addresses the inherent dynamics near the onset of unsteadiness, complementing experimental data of a large aircraft wing geometry with scale-resolving simulation. Specifically, delayed detached-eddy simulations are performed to reproduce the experiments with a Reynolds number of and a Mach number of 0.801, and they are analyzed using modal methods. Motivated by the recognized difficulty in simulating separating and reattaching shallow shear layers employing such techniques, the impact of two subgrid length-scale definitions are scrutinized. The simulations, using both the standard definition of maximum local spacing and a more recent vorticity-sensitive variant, are validated against experimental data from conventional steady and unsteady instrumentation and dynamic pressure-sensitive paint. Remarkable agreement is found between experiment and simulation when using the vorticity-sensitive definition, whereby dominant modal features extracted from surface pressures show striking similarity in describing localized pockets of shear-layer pulsation synchronized with an outboard-propagating shock oscillation. Identified modes from simulation cover broadband content centered at a Strouhal number of approximately 0.27, which is within the experimental frequency range characteristic for swept-wing shock buffet. Modes capturing an inboard-running lower-frequency shock unsteadiness centered at a Strouhal number of approximately 0.07 are exclusive to wind-tunnel conditions. Nevertheless, modal analysis succeeds in extracting key characteristics from such practical unsteady-flow datasets. It is anticipated that the current findings with help clarify these edge-of-the-envelope flow phenomena and ultimately inform shock-buffet delay and control strategies.

Fractal analysis of high speed rail geometry data: A case study of Ankara-Eskişehir high speed rail

Abstract By considering railway track geometry as a fractal pattern, the irregularity of the track geometry can be expressed numerically with the help of fractal dimensions. In this study, a novel algorithm based on ruler method has been developed and a program was written to determine the fractal dimensions of railway track geometry graphs. Four different fractal dimensions of DR1, DR2, DR3, and DR4 were proposed to determine the geometric irregularity of the railway track by using the data of Ankara-Eskisehir High-Speed Railway track. DR1 and DR2 were used to quantify short wavelength irregularities while DR3 and DR4 were used to quantify medium wavelength irregularities. At the end of the study, the relationship between fractal dimensions and standard deviation values, which is the quality indicator according to EN 13848-6, was investigated. According to this, there is a strong relationship between the DR3 and the standard deviation.

효율적 표면격자 생성을 위한 표면 곡률 근사화 기법

In this paper the issue of recovering surface curvature from the geometry data imported in the triangulated STL format for effective surface grid generation is described. Mathematical description and procedure to achieve division of a triangular Bezier surface by using patch point idea is given. The key issue in expressing a sub-region of triangular Bezier surface is to find the Barycentric coordinates of patch points of the sub-region. Once the coordinates of those Patch points are found, then the control points which are needed to describe the triangular Bezier surface for the sub-region can be routinely obtained. It is demonstrated that the quality of surface description along a geometry imported in STL format can be improved by using the current method of approximate curvature-recovery, which will eventually improve the accuracy and effectiveness of surface grid generated along the surface imported in STL format.

Prediction of elastic-plastic deformation of nanoporous metals by FEM beam modeling: A bottom-up approach from ligaments to real microstructures

For the prediction of elastic-plastic deformation behavior of nanoporous materials, a computationally efficient method is needed that integrates the complex 3D network structure and the large variation of ligament shapes in a representative volume element. Finite element simulations based on beam elements are most efficient, but for a quantitative prediction, a correction is required that accounts for the effects of the mass around the junctions. To this end, a nodal correction is presented that covers a wide range of parabolic-spherical ligament shapes. Smooth functions are provided that define the extension of the nodal corrected elements along the ligament axis, their radius and their yield stress as functions of the individual ligament shape. It is shown that the increase in radius of the nodal beam elements can be replaced by scaled material parameters. Simulations of randomized FEM beam networks revealed that, in relation to the randomization of the ligament axis, the distribution of the ligament shape has a stronger impact on the macroscopic stress–strain response and should thus receive particular attention during the characterization of nanoporous microstructures. This also emphasizes the importance of the thickness analysis via image processing. Combining a nodal corrected FEM beam model with geometry data derived from image multiplication of the skeleton and Euclidean distance transform of a nanoporous gold FIB-SEM tomography dataset significantly improves the prediction of the stress–strain curve. The remaining deviation is expected to stem from the known underestimation of the real ligament diameter by the Euclidean distance transform as well as local variations in the circularity of the ligaments.

Developing Reference Building for Campus Type Buildings in Universitas Gadjah Mada

The building sector has been the highest consumer in the total energy consumption in Indonesia, in which the building sector shared 39% of the national energy consumption. A university is a campus that consists of the landscapes and buildings with many kinds of academic and social activities that creates a type of energy consumption profile. This paper aims to demonstrate the importance of having a complete and reliable audit data of a university to develop reference building for campuses. The reference building serves as a building benchmark model for energy consumption, for 20 faculties in Universitas Gadjah Mada (UGM) with more than 200 buildings. The energy consumption model of the buildings is developed using the CLTD/SCL/CLF calculation method. Data of the building geometry, building envelope, and building profile are using data collected from a green campus assessment tools (GreenmetricUI) by walkthrough audit. The research compared the energy profile obtained from the audit data and the model calculated. It is shown that the energy profile model from the CLTD/SCL/CLF method is generally higher than the reported energy consumptions from electricity bills. It can be concluded that the main success in developing a reference building is by having a reliable audit data and a complete record of the electricity bills to validate the model. Once the energy profile obtained from the CLTD/SCL/CLF modeling is available, the data can be used for modelling reference building using energy model software.

A Markov switching regression analysis of freeway crash risks considering spatial effect

A Markov switching logit model with spatial dependencies for real-time crash risk assessment is proposed, with the purpose of identifying hazardous traffic-flow conditions with high crash potential. The Markov switching process assumes that freeway segments can switch between two unobserved safety states over time, and that parameter estimates may vary between these two states. The spatial simultaneous autoregressive process was used to account for possible dependencies in crash likelihood between neighbouring freeway segments. The proposed model was used to link crash likelihood with real-time traffic, weather and roadway geometry data. The Bayesian inference method based on Markov chain Monte Carlo simulations was used for model estimation. Bayes factor analysis suggested that the proposed model produces a better fit than other alternatives that ignore temporal or/and spatial effects. The estimation results revealed that two states with regard to crash likelihood exist and that freeway segments can switch between these states over time. The spatial autocorrelation coefficient indicated that crash likelihood on a freeway segment is interlinked with those on neighbouring segments over space. The key traffic variables contributing to crash likelihood are detector occupancy, occupancy difference between adjacent lanes, speed variance and occupancy difference between upstream and downstream detector stations. Moreover, three geometric variables and weather conditions are significantly related to crash likelihood in the model. The receiver operating characteristic curve showed that the predictive performance of the proposed model is satisfactory.