Cloud Model Research Articles

Integrated Performance Assessment of Prefabricated Component Suppliers Based on a Hybrid Method Using Analytic Hierarchy Process–Entropy Weight and Cloud Model

Prefabricated construction has garnered widespread attention worldwide. As an important part in purchasing management, prefabricated component suppliers (PCSs) undertake the bulk of the work and responsibility in the supply chain from on-site to off-site. Therefore, it is crucial and necessary to establish a decision-making framework to comprehensively evaluate the performance of PCSs. This study proposed a set of performance indicators for PCSs, including component quality, cost, delivery capability, service level, enterprise development potential, and enterprise cooperation potential. A hybrid method was established to evaluate the integrated performance of PCSs based on Analytic Hierarchy Process (AHP)–entropy weight and cloud model. It integrated the AHP and entropy weight method to calculate indicator weights, while the cloud model was employed to transform qualitative characteristics into quantitative ones. To verify the feasibility of this method, an empirical study was subsequently conducted using a typical case from China. The obtained results demonstrate that the overall performance of Supplier A lies at the “good” level, with the similarity index between the comprehensive cloud model and the standard cloud model within the good range, at 0.4045. Among the six primary indicators, quality performance scored the highest at 0.65, meeting the “excellent” standard. It can be seen that the hybrid approach of AHP–entropy weight and cloud model accurately and effectively demonstrates the integrated performance of PCSs. The main aim of this study was to establish a comprehensive system and develop a novel approach for evaluating the performance of PCSs within a hesitant fuzzy environment. The findings of this study can provide guidelines for researchers and the public to evaluate PCS performance, contributing significantly to the fields of supply chain management and construction engineering. Moreover, it provided a practical tool for professionals in the industry to enhance the supplier selection processes.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Real-Time Environmental Contour Construction Using 3D LiDAR and Image Recognition with Object Removal

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Post‐earthquake structural damage identification and safety evaluation using point clouds

AbstractEfficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

UAV-BIM-BEM: An automatic unmanned aerial vehicles-based building energy model generation platform

Many existing and aging buildings were in urgent need of upgrades and renovations, but lack accurate geometric and energy consumption information. This study introduced an unmanned aerial vehicles-based (UAV-based) automated platform for building information modellings (BIM) to generate builidng energy modellings (BEM), which was called the UAV-BIM-BEM platform. The platform enabled the creation of energy models for existing buildings to estimate energy consumption and perform retrofit analysis. A four-story office building in Hong Kong, with dimensions of 52.3 m in length, 14.3 m in width, and 16.5 m in height, was selected as the case study building. Firstly, using UAV oblique photography techniques and path planning algorithms, the building images were transformed into a detailed 3D point cloud model. The point cloud model was provided to Revit to generate the BIM model in IFC format. The IFC file containing the geometric information of the building was then supplied to the AutoBPS-BIM tool, resulting in the generation of the BEM model in EnergyPlus to simulate detailed end-use energy consumption. The annual electricity use intensity (EUI) of the building was estimated to be 145.05 kWh/m2. Lastly, the generated energy model was used to evaluate the energy-saving potential of three different measures. The results showed that the electrical energy savings from cooling equipment, lighting systems, and window replacement are 17.22 %, 6.91 %, and 6.61 %, respectively. This research demonstrated that the UAV-BIM-BEM platform has great potential in BEM modeling for existing buildings and building energy retrofitting.

Approaches to Disaster Recovery in Cloud Databases: A Comparative Analysis of Current Mechanisms

Disaster recovery (DR) is a critical aspect of maintaining the availability, integrity, and continuity of cloud databases, which store and manage vast amounts of mission-critical data. With the increasing reliance on cloud platforms for business operations, organizations must implement effective disaster recovery mechanisms to safeguard against data loss, downtime, and system failures. This paper presents a comparative analysis of current disaster recovery approaches in cloud databases, examining the strengths and weaknesses of various strategies, including backup and restore, data replication, and automated failover systems. We evaluate how these mechanisms are applied in different cloud models (public, private, and hybrid), considering factors such as recovery time objectives (RTO), recovery point objectives (RPO), cost, and scalability. The paper also discusses emerging technologies and trends in cloud DR, such as real-time data synchronization, machine learning-based predictive recovery, and multi-cloud disaster recovery solutions. Additionally, the role of cloud service providers’ Service Level Agreements (SLAs) in defining DR expectations is analyzed. By comparing the advantages and limitations of these mechanisms, this paper provides insights into how organizations can select the most appropriate DR strategy based on their specific business needs and risk tolerance. The study concludes by offering recommendations for optimizing disaster recovery plans in cloud environments to ensure business continuity and data protection in the face of unexpected disruptions.

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule.

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.
Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.
Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.
Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Navigation risk assessment for ocean-going ships in the north pacific ocean based on an improved dynamic Bayesian network model

As a central driver of global trade, the safety of navigation on ocean-going voyages holds paramount importance. This study improved dynamic Bayesian network model for comprehensive ocean-going ship risk assessment. Firstly, addressing the risk posed by harsh natural environments in ship navigation, information nodes from the three dimensions were selected, and a dynamic Bayesian network was incorporated to capture the dynamic information. Second, for the uncertainty problem of unstructured data, the dynamic Bayesian network was improved to effectively integrate multi-source information through the hesitant cloud model. Subsequently, a multi-level risk assessment framework was constructed to achieve a refined assessment for the risk changes under different human behaviors, ship vulnerability and navigational environment conditions. An empirical study of container ship accidents in the North Pacific Ocean verifies that the experimental model effectively captures dynamic information, enabling a more accurate determination of depression locations and consequently achieving a more precise navigation risk assessment. Furthermore, the model is capable of capturing seasonal variations in marine environmental risks within the study area. Consequently, the improved model furnishes a scientific foundation for devising targeted risk response strategies.

Prediction of dominant roof water inrush windows and analysis of control target area based on set pair variable weight - Forward correlation cloud model

Coal mining disturbs the roof overlying rock, forming a channel for roof water inrush. This process destroys the primary seepage state of the deep aquifer, resulting in waste and pollution of deep water resources and posing a significant threat to coal mining. Therefore, through field measurement and numerical simulation, this paper comprehensively analyzes the development law of coal seam mining overlying rock fracture, and determines the influence range of coal seam roof water inrush. Then, a set pair variable weight - forward associated cloud prediction model is constructed to predict the dominant water inrush window and seepage channel of coal seam roof, and the key protection area of coal mining roof water inrush is delineated. The results show that with the continuous advance of the coal face, the stress shifts to both sides of the face, forming a stress concentration area, and the vertical fissure develops intensively, which becomes the main channel of water inrush. After the development is stable, it is comprehensively determined that the fissure production ratio of the coal seam is 11.7, and the fracture zone extends to the aquifer area are the water inrush source. Then, through the prediction model, the edge of the danger area near the high-risk area is defined as the advantage window of coal seam roof water inrush, and the result is consistent with the actual water inrush event. Therefore, the target of water inrush in roof is determined, and the effective measures of water reduction mining are put forward. This study is of great significance for protecting deep water resources, ensuring mine safety, and creating efficiency and increasing income.

Trustworthy collaborative evaluation of multi-service subjects in the cloud manufacturing model

In the context of cloud manufacturing, challenges related to trust, including malicious deception and dishonest feedback, are exacerbated by information asymmetry among platform participants. To address these issues, a novel approach for evaluating collaboration credibility among multi-service subjects within the cloud manufacturing framework is introduced. Initially, an evaluation index system is constructed, incorporating both internal and external data from the platform. This system is framed around four critical dimensions: subject characteristics, service characteristics, product characteristics, and task characteristics. The attribute weights are determined using an integrated assignment method. Subsequently, to effectively address the issues of ambiguity, uncertainty and randomness of evaluation information in the integrated evaluation process, this paper proposed a comprehensive evaluation model. This model capitalizes on the strengths of intuitionistic fuzzy sets (IFSs) and cloud models in converting qualitative assessments into quantitative evaluations, and leverages the method of approximation of the order of ideal solutions (TOPSIS) to carry out a comprehensive assessment of the degree of trustworthy collaboration of the service subject. The practicality and validity of the proposed methodology are demonstrated through a case study analysis, which confirms the model's effectiveness in enhancing the reliability of collaborative evaluations under the cloud manufacturing model.

Assessment of Water Disaster Resilience in Mountainous Urban Metro Stations by Combination Weighting Method and Extension Cloud Model

Studying the resilience of metro stations in mountainous cities to heavy rain and flooding is of significant importance for enhancing the stability and safety of metro station operations. Considering the topographical and climatic characteristics of mountainous urban areas, this study analyzes the mechanisms through which heavy rain and flooding affect metro station resilience. Based on this analysis, 27 factors, influencing metro station resilience, are identified across 4 dimensions: absorptive capacity, resistance capacity, recovery capacity, and adaptive capacity. A water disaster resilience evaluation index system and corresponding rating standards are established for metro stations in mountainous cities. By combining the advantages of objective and subjective weighting, the combination weights of evaluation indicators are calculated using game theory. The extension theory is combined with the cloud model to establish a model for assessing the water disaster resilience of metro stations in mountainous urban areas. The applicability and feasibility of the model are validated through its implementation at Shapingba Station within Chongqing Rail Transit. The evaluation results obtained from the established model indicate a resilience level of IV for Shapingba metro station, reflecting a high level of resilience that aligns with real-world conditions. These findings further validate the proposed evaluation standards and the method for assessing the water disaster resilience of metro stations based on the combination weighting method and extension cloud model. This evaluation method considers the uncertainty in the evaluation process, demonstrating good feasibility and reliability. It offers a new perspective and methodology for assessing and analyzing the resilience of similar metro stations in mountainous cities.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

Evaluating the water resistance performance of coal seam floor rock mass using VW–NC model

The rock mass of coal seam floor plays a crucial role in blocking the uplift and runoff of the underlying confined water in the course of mining under water pressure. Accurately assessing its water resistance performance, which is influenced by numerous factors, is important for preventing accidents caused by inrushing water. This study focuses on a coal mine in the Ningwu coalfield in North China. We establish an evaluation system for the water resistance performance of coal seam floor rock mass, using indices including rock mass thickness, lithological combination characteristics, rock mass quality, structure complexity and bedding plane count. A variable weight–normal cloud (VW–NC) model is formulated to quantitatively evaluate the water resistance performance of the coal seam floor rock mass. Comparative analysis with actual mining conditions and other models reveals that the VW-NC model provides the most precise and accurate results. The study finds that the primary influencing factors on the water resistance performance are weak structural planes, including faults, joints, and bedding planes. The proportions of strong, relatively strong, relatively weak, and weak zones in research area are 20.41%, 59.88%, 12.38%, and 7.33%, respectively.

Evaluation of Spatial and Temporal Distribution of Carbon Emissions in Power Grid Based on Cloud Theory

In order to clearly determine the carbon emission distribution of regions, lines or nodes in the power grid, this paper applies cloud theory to the evaluation of the distribution of carbon emissions in the power grid. Based on the theory of carbon emission flow in the whole life cycle, five indicators that can reflect the spatial and temporal distribution of carbon emissions are constructed from the two dimensions of space and time. Cloud theory is used to establish the standard cloud of the carbon emission distribution level to quantify the randomness and fuzziness of the data to be evaluated. The bilateral constraint cloud theory and data-driven cloud transformation are combined to construct five comprehensive standard clouds of excellent, good, medium, poor and inferior, which are used as the evaluation interval of the evaluation index of carbon emission distribution. The reverse cloud is used to convert multiple sets of data into cloud droplets. Through the similarity measurement algorithm based on cloud model overlap, the comprehensive evaluation level of carbon emission distribution state in the time dimension is determined. Taking the IEEE 39 system as the research object, the spatial and temporal distribution of carbon emissions is evaluated, and the rationality and effectiveness of the proposed model are verified. Finally, the influence of the new energy penetration rate and power supply structure on the carbon emission distribution of the power grid is discussed by using cloud computing. Based on this, the targeted carbon reduction strategies for different types of nodes and the method of measuring the optimal new energy penetration rate are proposed and can provide a decision-making reference for optimizing the carbon emissions of the power grid.

Does the HCN/CO ratio trace the fraction of gravitationally bound gas? II. Variations in CO and HCN emissivity

We modeled emissivities of the HCN and CO $J=1-0$ transitions across a grid of molecular cloud models encapsulating observed properties that span from normal star-forming galaxies to more extreme merging systems. These models are compared with archival observations of the HCN and CO $J=1-0$ transitions, in addition to the radio continuum at 93 GHz, for ten nearby galaxies. We combined these model emissivities with the predictions of gravoturbulent models of star formation presented in the first paper in this series. In particular, we explored the impact of excitation and optical depth on CO and HCN emission and assess if the HCN/CO ratio tracks the fraction of gravitationally bound dense gas, $f_ grav $, in molecular clouds. We find that our modeled HCN/CO ratios are consistent with the measurements within our sample, and our modeled HCN and CO emissivities are consistent with the results of observational studies of nearby galaxies and clouds in the Milky Way. CO emission shows a wide range of optical depths across different environments, ranging from optically thick in normal galaxies to moderately optically thin in more extreme systems. HCN appears only moderately optically thick and shows significant subthermal excitation in both normal and extreme galaxies. We find an anticorrelation between HCN/CO and $f_ grav $, which implies that the HCN/CO ratio is not a reliable tracer of $f_ grav $. Instead, this ratio appears to best track gas at moderate densities ($n>10^ cm $), which is below the typically assumed dense gas threshold of $n>10^ cm $. We also find that variations in CO emissivity depend strongly on optical depth, which is a product of variations in the dynamics of the cloud gas. HCN emissivity is more strongly dependent on excitation, as opposed to optical depth, and thus does not necessarily track variations in CO emissivity. We further conclude that a single line ratio, such as HCN/CO, will not consistently track the fraction of gravitationally bound, star-forming gas if the critical density for star formation varies in molecular clouds. This work highlights important uncertainties that need to be considered when observationally applying an HCN conversion factor in order to estimate the dense (i.e.,\ cm $) gas content in nearby galaxies.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

EVALUACIÓN EN LA NUBE DE LA CAPACIDAD DE GESTIÓN DE EMERGENCIAS DEL GOBIERNO ANTE CATÁSTROFES POR INUNDACIONES URBANAS: DATOS DE CHINA

A relevant evaluation index system was established in this study for the objective measurement of emergency management capability for urban flood disasters based on the four stages of emergency response, namely, “prevention, early warning, response, and recovery.” The weight distribution of different evaluation indices was calculated by using the CRITIC method, which generated an evaluation cloud and a result cloud based on the cloud model. A case study on the emergency management of Zhengzhou City in Henan Province of the “7.20 Flood Disaster” was conducted, and the evaluation results were displayed intuitively as the digital features of the cloud model and in the computational cloud diagrams. The evaluation results of the emergency management capability of Zhengzhou City for the “7.20 Flood Disaster” were obtained, and a new direction for emergency management capability improvement was proposed based on a capability enhancement priority matrix. Results showed that Zhengzhou City exhibited only the “simple level - system management mode” in coping with the “7.20 Flood Disaster.” Zhengzhou City can improve its emergency management capability against flood disasters by improving the reliability of its monitoring and communication systems, the effectiveness of its publicity and education measures, and the timeliness of its provision of postdisaster information statistics and reports. The conclusions disclose a new direction for the scientific judgment of emergency management capability for flood disasters and the formulation of capability enhancement strategies. Key Words: Flood disasters; emergency management capability; cloud model; CRITIC method; capability enhancement matrix

Modeling Nonspherical Hailstones

Abstract Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes. Significance Statement In numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes.

Calibration of multi-robot coordinates for collaborative wire arc additive manufacturing using cross-source 3D point cloud models

To increase the deposition deficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.

Effects of the internal temperature on vertical mixing and on cloud structures in ultra-hot Jupiters

Context. The vertical mixing in hot-Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of cold trapping of refractory species in the deep atmosphere. Aims. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of ultra-hot Jupiters (UHJs) and the spacial distribution of cloud particles across the planet. Methods. We combined a simplified tracer-based cloud model, a picket fence radiative-transfer scheme, and a mixing length theory to the Exo-FMS general circulation model. We ran the model for five different internal temperatures at typical UHJ atmosphere system parameters. Results. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer gets formed below the radiative-convective boundary (RCB) in the cooler cases. Conclusions. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective mixing plays a minor role compared to advective mixing in keeping cloud particles aloft in UHJs with warm interiors. Higher vertical turbulent heat fluxes and the advection of potential temperature inhibit convection in warmer interiors. Our results suggest that isolated upper atmosphere regions above cold interiors may exhibit strong convective mixing in isolated regions around Rossby gyres, allowing aerosols to be better retained in these areas.