Geometry Data Research Articles

Featured Collection Introduction: National Water Model III

Featured Collection Introduction: National Water Model III

Improvement of the Autodriver Algorithm for Autonomous Vehicles Using Roll Dynamics

The autodriver algorithm was introduced as a path-following algorithm for autonomous vehicles, which uses road geometry data and planar vehicle dynamics. In this paper, the autodriver algorithm is improved according to practical implications, and a more realistic vehicle model (roll mode) is used, which considers roll degree of freedom in addition to a planar motion. A Ghost-Car path-following approach is introduced to define the desired location of the car at every instance. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses, are discussed and used to construct a feed-forward component of a path-following controller based on the autodriver algorithm. Feedback control loops are designed and applied to minimise transient errors between the road and vehicle positions. Finally, simulations are performed to analyse the path-following performance of the proposed scheme. The results show promising performance of the controller both in terms of error minimisation and passenger comfort.

Energy optimization of a micro-CHP engine using 1-D and 3-D modeling

This research focused on utilizing numerical simulation tools to improve the performance of a micro-CHP engine. The engine was developed at West Virginia University by screening candidate technologies and implementing those which were balanced between practicality and cost. The engine was a 34-cc, two-stroke engine which was modified to operate on low-pressure direct injection of natural gas combined with resonant intake and exhaust systems. This engine served as a baseline engine design. A 1-D simulation was developed and trained based on the baseline engine geometry and experimental data collected from laboratory experiments. After verifying the 1D model with measured data from the baseline configuration, a genetic algorithm approach was used to optimize the exhaust resonator design such that the fuel efficiency was maximized. Based on simulation outcomes, a new exhaust resonator was fabricated and tested. The experimental results showed an 8.3% improvement in brake thermal efficiency (BTE) compared to the baseline design. The test results of the optimized exhaust design were used in a 3-D CFD cold flow model to optimize the spark plug location to exploit charge stratification. The 3-D simulations suggested an alternative spark plug location, which was then applied on the engine and improved results were verified with additional experimental operation. The experimental results showed relative increase in BTE of 5.7% and a 4% decrease in total unburnt fuel compared to the original spark location.

JCOGIN: a programming framework for particle transport on combinatorial geometry

Domain-specific programming frameworks are usually effective to simplify the development of large-scale applications on supercomputers. This paper introduces a parallel programming framework named JCOGIN for particle transport on combinatorial geometry. JCOGIN provides a combinatorial geometry data model and a patch-based parallel computing model to manage the data distribution in parallel computing and implements the hybrid parallelism of the domain decomposition and the particle parallelism on MPI/OpenMP to overcome the bottleneck of huge memory demand and long computational time. The application programming interface of JCOGIN can support users to quickly develop their parallel particle transport applications. Based on this framework, users only need to write serial codes for large-scale numerical simulations on modern supercomputers. The parallel efficiency of applications based on JCOGIN can reach up to 80% on hundreds of thousands of CPU cores.

Fault2SHA Central Apennines database and structuring active fault data for seismic hazard assessment

We present a database of field data for active faults in the central Apennines, Italy, including trace, fault and main fault locations with activity and location certainties, and slip-rate, slip-vector and surface geometry data. As advances occur in our capability to create more detailed fault-based hazard models, depending on the availability of primary data and observations, it is desirable that such data can be organized in a way that is easily understood and incorporated into present and future models. The database structure presented herein aims to assist this process. We recommend stating what observations have led to different location and activity certainty and presenting slip-rate data with point location coordinates of where the data were collected with the time periods over which they were calculated. Such data reporting allows more complete uncertainty analyses in hazard and risk modelling. The data and maps are available as kmz, kml, and geopackage files with the data presented in spreadsheet files and the map coordinates as txt files. The files are available at: https://doi.org/10.1594/PANGAEA.922582.

Analisa Keruntuhan Bendungan Kering Ciawi Menggunakan Program HEC-RAS dan InaSAFE

Dam is a structure built across a stream to retain water for the purpose of water supply, irrigation, hydropower, and flood control. Beside the high benefits, dams are also a potential threat to public safety, which is dam break/dam failure. The purpose of this study was to discover various scenarios of dam break at Ciawi Dam, with the main modes of failure have been identified as piping or overtopping. Ciawi dam break are modeled and analyzed using HEC-RAS v5.0.7 based on available geometry data. After that, based on regional population data, the economic loss calculated using InaSAFE. The simulation results showed that the Probable Maximum Flood (PMF) with a peak discharge of 1845,867 m3/s did not generate an overtopping scenario. From the analysis of 2-D simulation in HEC-RAS, the Ciawi dam break due to piping at Probable Maximum Flood (PMF) has a maximum water depth of 46,419 m with the area of inundation covers 108,04 km2. Those numbers imply that Ciawi Dam break classified as a high-hazard category.

Are OpenStreetMap building data useful for flood vulnerability modelling?

Abstract. Flood risk modelling aims to quantify the probability of flooding and the resulting consequences for exposed elements. The assessment of flood damage is a core task that requires the description of complex flood damage processes including the influences of flooding intensity and vulnerability characteristics. Multi-variable modelling approaches are better suited for this purpose than simple stage–damage functions. However, multi-variable flood vulnerability models require detailed input data and often have problems in predicting damage for regions other than those for which they have been developed. A transfer of vulnerability models usually results in a drop of model predictive performance. Here we investigate the questions as to whether data from the open-data source OpenStreetMap is suitable to model flood vulnerability of residential buildings and whether the underlying standardized data model is helpful for transferring models across regions. We develop a new data set by calculating numerical spatial measures for residential-building footprints and combining these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional subsets and are tested for predicting flood damage in other regions. This regional split-sample validation approach reveals that the predictive performance of models based on OpenStreetMap building geometry data is comparable to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of residential-building vulnerability. The transfer of these models for application in other regions should include a test of model performance using independent local flood data. Including numerical spatial measures based on OpenStreetMap building footprints reduces model prediction errors (MAE – mean absolute error – by 20 % and MSE – mean squared error – by 25 %) and increases the reliability of model predictions by a factor of 1.4 in terms of the hit rate when compared to a model that uses only water depth as a predictor. This applies also when the models are transferred to other regions which have not been used for model learning. Further, our results show that using numerical spatial measures derived from OpenStreetMap building footprints does not resolve all problems of model transfer. Still, we conclude that these variables are useful proxies for flood vulnerability modelling because these data are consistent (i.e. input variables and underlying data model have the same definition, format, units, etc.) and openly accessible and thus make it easier and more cost-effective to transfer vulnerability models to other regions.

Technical feasibility of integrating a modified power conversion unit into a non‐nuclear microreactor testbed

This work analyzes the feasibility of integrating a modified commercial Capstone C30 microturbine unit into a non-nuclear testbed developed for performance evaluation of microreactor design concepts. Testing will be conducted to provide detailed reactor core and heat removal section thermal hydraulic performance data for prototypical geometries and operating conditions and demonstrate integration with relevant power conversion units. The modified C30 power conversion unit (PCU) uses external electrical heating, rather than fossil fuel combustion, to provide a maximum power output of ~30 kWe in a closed Brayton cycle (CBC) loop using nitrogen as the working fluid. A 75 kWt electrically heated test article was evaluated as the heat source to provide a turbine inlet temperature of 600°C to the modified C30 PCU. The operation of a test article supplying a maximum thermal power of 250 kWt was also considered. The analyses performed here indicate that the CBC PCU will be capable of operating over a range of steady state and transient conditions to evaluate test article heat transfer performance in representative operational scenarios. Normal, off-design operation and dynamic stability of the PCU are analyzed, and a proposed set of tests of the power control schemes and heat source-PCU coupling are outlined. This study supports the timely development of microreactors intended for deployment by the end of this decade.

LOW ALTITUDE PHOTOGRAMMETRY FOR URBAN ROAD MAPPING

Road connection has high impact on the city development. It helps boost the economic environment along the road. Therefore, it is important to maintain and divide by their traffic flow and road reserve well to determine the privilege of maintenance and budget contribution for every year. Road opens up the relation of intercity and urban as it gives the impact of development along the road. To manage road over the country, geometry data of road is needed for decision making and project well management. The primary data is usually contributed by field technical support persons, such as surveyor, engineer, and others for conventional method of survey, image along the road, computer aid drawing (cad data) as built drawing or topographical plan, and others. This study proposes an urban road mapping with optimal flight parameter and flying low for detail texture acquisition of feature. It ensures the high efficiency, low cost, short cycle, strong maneuverability, convenient operation, and others of a product. The objective of the project is to determine the optimal flight parameter in mapping out a road feature inside the road reserve with detailed digital orthophoto model (DOM) and digital elevation model (DEM). The flight parameters of unmanned aerial vehicle (UAV) and requirements for focal length effectiveness, flight planning preparation, image lap percentage, UAV altitude, and ground control point (GCP) distribution setup were outlined. The study investigated the effect of different focal length effects, GCP shape-based network (pyramid square-, square-, and linear-based networks), UAV altitude (90m, 65m, and 35m), and end lap percentage of image (90%, 80%, and 70%) on the photogrammetry-derived product. The 95m and 65m altitudes gave the lowest root mean square error (RMSE) value (±5cm horizontal and ±8cm vertical). In addition, 80% consistently showed the lowest RMSE for all end lap percentage options. Meanwhile, the pyramid square-based network recovered a total of 40% accuracy higher than square- and linear-based networks. This study could help the local authorities to implement smart road maintenance within their region.

Relationship between the form and function of implant design in total knee replacement

The implant design in total knee replacement affects postoperative functionality greatly, therefore, its optimization is of major concern. However, little is known about how implant design parameters affect active knee kinematics.Comprehensive in silico and in vitro sensitivity analyses were performed, based on one patient-specific, physical knee implant set and corresponding bone and knee implant surface geometry data. The implant surfaces were parametrized and varied systematically, resulting in 85 different knee implant surface models. In addition, four variations of extensor mechanism parameters, being the muscular attachment points defining the Q-Angle, were investigated. The variations were evaluated in a patient-specific multibody simulation model and an experimental testing rig and contributions of different implant designs and extensor mechanism parameters on kinematics were analysed.The results of the in silico and in vitro analyses showed good qualitative agreement. The highest deviations from the implant’s reference kinematics were found for parameter variations of the femoral sagittal radii, the lateral trochlear elevation, the tibial sagittal slopes, the mediolateral position of the patellar ridge and the mediolateral position of the tuberositas tibiae.The implant design parameters identified with the highest functional relevance should be focused on in implant design. As the tuberositas tibiae’s position constituted a main impact factor, it should also be considered during implant design and preoperative planning. Due to the competing influence of implant design parameters on active kinematics, respective parameters should be designed which are compatible to each other to avoid adverse constraints and associated functional limitations.

A note on the Cayley-Menger determinant and the Molecular Distance Geometry Problem

Using information from protein geometry and distance data provided by Nuclear Magnetic Resonance (NMR) experiments, the Molecular Distance Geometry Problem (MDGP) can be solved by a combinatorial approach, called Branch-and-Prune (BP). The primal version of BP algorithm seeks MDGP graph realizations, while the dual BP looks for completions of associated partial distance matrices. These two algorithms are very similar when distance values are precise. In the literature, there are some proposals for extending the primal BP to take care of NMR uncertainties. Using Cayley-Menger determinant, we present a global optimization approach that also allows the dual BP to deal with NMR uncertainties.

Lubricated Friction and the Hersey Number.

Most frictional contacts are lubricated in some way, but is has proven difficult to measure and predict lubrication layer thicknesses and assess how they influence friction at the same time. Here we study the problem of rigid-isoviscous lubrication between a plate and a sphere, both experimentally and theoretically. The liquid layer thickness is measured by a novel method using inductive sensing, while the friction is measured simultaneously. The measured values of the layer thickness and friction on the disk are well described by the hydrodynamic description of liquid flowing through a contact area. This allows us to propose a modified version of the Hersey number that compares viscous to normal forces and allows us to rescale data for different geometries and systems. The modification overcomes the shortcomings of the commonly used Hersey number, adds the effects of the geometry of the configuration on the friction, and successfully predicts the lubrication layer thickness.

Monte Carlo modeling of the influence of strong magnetic fields on the stem-effect in plastic scintillation detectors used in radiotherapy dosimetry.

To investigate the impact of strong magnetic fields on the stem-effect in plastic scintillation detectors (PSDs) using Monte Carlo methods. Prior to building the light guide model, the properties of the Čerenkov process in GEANT4 were investigated by simulating depth-dose and depth-Čerenkov emission profiles in water as functions of Čerenkov process input parameters. In addition, profile simulations were performed for magnetic field strengths ranging from 0T to 1.5T. A PMMA light guide was constructed in GEANT4 using data from the manufacturer and literature. Simulations were performed with the model as functions of depth and fiber-beam angle where the simulated stem-effect spectrum and the Čerenkov light ratio (CLR) were scored and compared to measured data in the literature. The light guide optical properties were iteratively adjusted until agreement between the simulated and measured data was achieved. Simulations were performed with the validated model as functions of depth and magnetic field strength and the simulated data were compared to measured data in the literature. The model was also used to evaluate the sensitivity of the CLR to the various optical properties of the light guide in different irradiation conditions. No significant changes in the depth-dose or depth-Čerenkov emission profiles were observed with step-size restrictions imposed by the Čerenkov process input parameters, which was attributed to the condensed history algorithm and transport parameters used in this work. Similar changes in the depth-dose and depth-Čerenkov emission profiles were observed with increasing magnetic field strength, which indicates the Čerenkov process is not adversely impacted by the presence of the magnetic field. Following optimization of the light guide optical properties, agreement within two standard deviations was observed between the simulated and measured optical data for all validation geometries considered. Agreement within one standard deviation was observed between the simulated and measured data for all depths and field strengths ≥0T whereas discrepancies were observed for magnetic field strengths <-0.35T. These significant differences were attributed to insufficient measurement data for this irradiation configuration during model validation. Of the light guide optical properties investigated, the fluorescence signal had the greatest impact on the CLR sensitivity to the magnetic field. No significant change in the Čerenkov emission per dose in water was observed for magnetic field strengths up to 1.5T. The nominal fiber fluorescence signal was found to have a significant impact on the CLR sensitivity to varying irradiation conditions where changes up to 11.7% were observed whereas the mirror reflectivity and fiber attenuation had a modest impact with maximum CLR changes of 2.6% and 1.2% relative to 0T, respectively. The results of this work suggest light guides with low fiber fluorescence should be used with PSDs for dosimetry measurements in magnetic fields to minimize the impact of the magnetic field on the CLR correction.

Hybrid rail track quality analysis using nonlinear dimension reduction technique with machine learning

Track geometry parameters from rail track inspection are regulated within unique safety limits for different track classes. This study focuses on developing an index that combines safety and track quality because of the inefficiency of corrective maintenance activities between routine maintenance cycles when federal geometry limits are violated. This combination is achievable by summarizing multivariate track geometry parameters as an improvement to previous linear approaches to address the problem of inefficient track geometry maintenance programs. The use of nonlinear dimension reduction (T-stochastic neighbor embedding (T-SNE)) for hybrid track quality index development and the influence of time-based parameters on track quality is evaluated in this study. The results show that the probability of geometry defects is correlated with principal components, but T-SNE had the best prediction on train-test splits despite its poor performance on a blind validation set. The absence of an observable correlation between the track geometry and acceleration data requires further investigation.

Voxel-based finite element modelling of wood elements based on spatial density and geometry data using computed tomography

Abstract In this paper, voxel-based finite element modelling based on spatial geometry and density data is applied to simulate the detailed stress and strain distribution in a large wood element. As example, a moulded wooden tube with a length of 3 m and a diameter of 0.3 m is examined. Gamma-ray computed tomography is used to obtain both, its actual geometric shape and spatial density distribution. Correlation functions (R2 ≈ 0.6) between density and elastic material properties are experimentally determined and serve as link for defining the non-uniform distribution of the material properties in the finite element model. Considering the geometric imperfections and spatial variation of the material properties, a detailed analysis of the stress and strain distribution of a wood element is performed. Additionally, a non-destructive axial compression test is applied on the wooden tube to analyse the load-bearing behaviour. By means of digital image correlation, the deformation of the surface is obtained, which also serves for validation of the finite element model in terms of strain distributions.

Flood Inundation Mapping of Jigjiga-Town and Its Surrounding Environment: Using GIS &amp; HEC-RAS Model

Flood is one of the natural hazards that have negative impacts on peoples and property. It occurs in lowland areas without any sign when excessive rains fall in adjacent highland areas. Since flood is unavoidable natural phenomena, adopting protective mechanisms are crucial. This study was conducted in Jigjiga town, Eastern Ethiopia with main objective of delineating flood risk areas. For the purpose of flood risk mapping preparation, hydrology and hydraulics models have been used. Besides, land use /cover change detection analysis has been carried out between the year 2003 and 2017. Soil Conservation System (SCS) Curve Number method was used as hydrology model. Satellite and field data have been collected, filtered and finally a geo-database has been developed in GIS environment. Flow data and river geometry data, which obtained from hydrology model and prepared using HEC-GeoRAS was used as inputs for HEC-RAS model. Before running steady flow analysis, correction of geometry data has been carried out in HEC-RAS environment. From the study, it was found that, expansion of built up area and agricultural land at the cost of open area. Flood inundation extent maps show that increasing in aerial coverage as return period increases but the variation in area between consecutive periods are small. The flood extent obtained by applying structural flood control mechanism shows that the measure reduces the flood extent considerably but it also exhibits some part of the town is still in undated in all return periods. From the analysis it was observed that the increases in built up area and agricultural land may lead to increase flooding. Besides, among the LULC classes fall under high risk zone at different return periods, built up area and agricultural land consist a considerable proportion. It also observed that, flood risk areas increase as return period increases but the variation between the periods is small. The flood risk map can be used for planning control measures for future flood.

Liquid Argon Instrumentation and Monitoring in LEGEND-200

LEGEND is the next-generation experiment searching for the neutrinoless double beta decay in 76Ge. The first stage, LEGEND-200, takes over the cryogenic infrastructure of GERDA at LNGS: an instrumented water tank surrounding a 64 m3 liquid argon cryostat. Around 200 kg of Ge detectors will be deployed in the cryostat, with the liquid argon acting as cooling medium, high-purity passive shielding and secondary detection medium. For the latter purpose, a liquid argon instrumentation is developed, based on the system used in GERDA Phase II. Wavelength shifting fibers coated with TPB are arranged in two concentric barrels. Both ends are read out by SiPM arrays. A wavelength shifting reflector surrounds the array in order to enhance the light collection far from the array. The LLAMA is installed in the cryostat to permanently monitor the optical parameters and to provide in-situ inputs for modeling purposes. The design of all parts of the LEGEND-200 LAr instrumentation is presented. An overview of the geometry, operation principle, and off-line data analysis of the LLAMA is shown.

A Soft Pressure Sensor Skin to Predict Contact Pressure Limit Under Hand Orthosis.

Customized static orthoses in rehabilitation clinics often cause side effects, such as discomfort and skin damage due to excessive local contact pressure. Currently, clinicians adjust orthoses to reduce high contact pressure based on subjective feedback from patients. However, the adjustment is inefficient and prone to variability due to the unknown contact pressure distribution as well as differences in discomfort due to pressure across patients. This paper proposed a new method to predict a threshold of contact pressure (pressure limit) associated with moderate discomfort at each critical spot under hand orthoses. A new pressure sensor skin with 13 sensing units was configured from FEA results of pressure distribution simulated with hand geometry data of six healthy participants. It was used to measure contact pressure under two types of customized orthoses for 40 patients with bone fractures. Their subjective perception of discomfort was also measured using a 6 scores discomfort scale. Based on these data, five critical spots were identified that correspond to high discomfort scores (>1) or high pressure magnitudes (>0.024 MPa). An artificial neural network was trained to predict contact pressure at each critical spot with orthosis type, gender, height, weight, discomfort scores and pressure measurements as input variables. The neural networks show satisfactory prediction accuracy with R2 values over 0.81 of regression between network outputs and measurements. This new method predicts a set of pressure limits at critical locations under the orthosis that the clinicians can use to make orthosis adjustment decisions.

L3DOC: Lifelong 3D Object Classification.

3D object classification has been widely applied in both academic and industrial scenarios. However, most state-of-the-art algorithms rely on a fixed object classification task set, which cannot tackle the scenario when a new 3D object classification task is coming. Meanwhile, the existing lifelong learning models can easily destroy the learned tasks performance, due to the unordered, large-scale, and irregular 3D geometry data. To address these challenges, we propose a Lifelong 3D Object Classification (i.e., L3DOC) model, which can consecutively learn new 3D object classification tasks via imitating "human learning". More specifically, the core idea of our model is to capture and store the cross-task common knowledge of 3D geometry data in a 3D neural network, named as point-knowledge, through employing layer-wise point-knowledge factorization architecture. Afterwards, a task-relevant knowledge distillation mechanism is employed to connect the current task to previous relevant tasks and effectively prevent catastrophic forgetting. It consists of a point-knowledge distillation module and a transforming-space distillation module, which transfers the accumulated point-knowledge from previous tasks and soft-transfers the compact factorized representations of the transforming-space, respectively. To our best knowledge, the proposed L3DOC algorithm is the first attempt to perform deep learning on 3D object classification tasks in a lifelong learning way. Extensive experiments on several point cloud benchmarks illustrate the superiority of our L3DOC model over the state-of-the-art lifelong learning methods.

3D finite element model of dynamic material behaviors for multilayer ultrasonic metal welding

Ultrasonic metal welding (UMW) has been widely applied as a high throughput solid-state joining technology for multilayers of sheet metal. During a typical UMW process, multilayer work materials are mechanically compressed by a knurl-patterned horn (also known as a sonotrode) onto an anvil tool, and a simultaneous in-plane sliding is applied to the horn at an ultrasonic frequency (20 kHz or higher) to help form the weld at the material interfaces. There is a great challenge in modeling and simulating the dynamic behavior of the work material and the whole weld formation process is subject to ultrasonic mechanical loadings imposed by the knurl-patterned horn tool. In this work, finite element (FE) models are developed to simulate the multilayer UMW process using knurl-patterned tools by directly applying the ultrasonic vibration as a model input. For a short weld duration of 0.1∼0.5 s, a high-fidelity FE modeling approach is developed using ABAQUS/Explicit to simulate the dynamic material response under the 20 kHz horn vibration. For an extended long welding duration of approximately 1.0 s, a computationally efficient hybrid approach is developed using both ABAQUS/Explicit and DEFORM-3D in order to leverage the strengths of each software package. The developed models are validated using experimental data of dynamic welding force, temperature, and weld geometry from in-situ process measurements of UMW. The 3D FE models developed in this study are the most comprehensive solution to date to simulate the complex material response subject to UMW process conditions and provide engineering guidance for the design of UMW applications.