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Добірка наукової літератури з теми "DENSITY VARYING CLUSTERS"

Автор: Grafiati

Опубліковано: 19 січня 2024

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Статті в журналах з теми "DENSITY VARYING CLUSTERS"

Zhang, Yongzhong, Hexiao Huang, Jie Du, and Yan Ma. "RFDPC: Density Peaks Clustering Algorithm Based on Resultant Force." Mathematical Problems in Engineering 2022 (June 29, 2022): 1–19. http://dx.doi.org/10.1155/2022/9143727.

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Density peaks clustering (DPC) is an efficient and effective algorithm due to its outstanding performance in discovering clusters with varying densities. However, the quality of this method is highly dependent on the cutoff distance. To improve the performance of DPC, the gravitation-based clustering (GDPC) algorithm is proposed. However, it cannot identify the clusters of varying densities. We developed a novel density peaks clustering algorithm based on the magnitude and direction of the resultant force acting on a data point (RFDPC). RFDPC is based on the idea that the resultant forces acting on the data points in the same cluster are more likely to point towards the cluster center. The cluster centers are selected based on the force directional factor and distance in the decision graph. Experimental results indicate superior performance of the proposed algorithm in detecting clusters of different densities, irregular shapes, and numbers of clusters.

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Dr Bhadresh R. Pandya, Bhumika S. Arora, Dr Vijay Chavda,. "PxEBCA: Proximity Expansion Based Clustering Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 852–56. http://dx.doi.org/10.17762/turcomat.v12i5.1494.

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Cluster analysis is one of the main techniques for analysing data. It is a technique for detecting groups of objects which are similar without specifying any criteria for the grouping. The matter of detecting clusters is challenging when the clusters are of varied size, density and shape. DBSCAN can find arbitrary shaped clusters along with outliers but it cannot handle different density. This paper presents a new method for detecting density based clusters which works on datasets having varied density. In this paper we propose PxEBCA that discovers clusters with arbitrary shape and also with varying density.Experimental evaluation of the effectiveness and efficiency of PEBCA was done using synthetic data. The results of experiments demonstrated that PxEBCA is significantly more effective in discovering clusters of arbitrary shapes with varying densities.

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Xie, Yiqun, Xiaowei Jia, Shashi Shekhar, Han Bao, and Xun Zhou. "Significant DBSCAN+: Statistically Robust Density-based Clustering." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–26. http://dx.doi.org/10.1145/3474842.

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Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

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Sarkar, Anish. "Finite clusters in high density Boolean models with balls of varying sizes." Advances in Applied Probability 30, no. 4 (December 1998): 929–47. http://dx.doi.org/10.1239/aap/1035228201.

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In this paper we study finite clusters in a high density Boolean model with balls of two distinct sizes. Alexander (1993) studied the geometric structures of finite clusters in a high density Boolean model with balls of fixed size and showed that the only possible structure admitted by such events is that all Poisson points comprising the cluster are packed tightly inside a small sphere. When the balls are of varying sizes, the event that the cluster consists of k1 big balls and k2 small balls (both k1, k2 ≥ 1) occurs only when the centres of all big balls are compressed in a small sphere and the centres of the small balls are distributed uniformly inside the region formed by the big balls in such a way that the small balls are totally contained inside the big balls. We also show that it is most likely that a finite cluster in a high density Boolean model with varying ball sizes is made up only of small balls.

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Sarkar, Anish. "Finite clusters in high density Boolean models with balls of varying sizes." Advances in Applied Probability 30, no. 04 (December 1998): 929–47. http://dx.doi.org/10.1017/s0001867800008739.

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Анотація:

In this paper we study finite clusters in a high density Boolean model with balls of two distinct sizes. Alexander (1993) studied the geometric structures of finite clusters in a high density Boolean model with balls of fixed size and showed that the only possible structure admitted by such events is that all Poisson points comprising the cluster are packed tightly inside a small sphere. When the balls are of varying sizes, the event that the cluster consists of k 1 big balls and k 2 small balls (both k 1, k 2 ≥ 1) occurs only when the centres of all big balls are compressed in a small sphere and the centres of the small balls are distributed uniformly inside the region formed by the big balls in such a way that the small balls are totally contained inside the big balls. We also show that it is most likely that a finite cluster in a high density Boolean model with varying ball sizes is made up only of small balls.

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Tan, Lu, and Joanna Crowe Curran. "Comparison of Turbulent Flows over Clusters of Varying Density." Journal of Hydraulic Engineering 138, no. 12 (December 2012): 1031–44. http://dx.doi.org/10.1061/(asce)hy.1943-7900.0000635.

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Shen, Xiaoqi, Wenzhong Shi, Zhewei Liu, Anshu Zhang, Lukang Wang, and Fanxin Zeng. "Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities." ISPRS International Journal of Geo-Information 11, no. 7 (July 13, 2022): 397. http://dx.doi.org/10.3390/ijgi11070397.

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Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

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Pearce, Francesca A., Scott T. Kay, David J. Barnes, Richard G. Bower, and Matthieu Schaller. "Hydrostatic mass estimates of massive galaxy clusters: a study with varying hydrodynamics flavours and non-thermal pressure support." Monthly Notices of the Royal Astronomical Society 491, no. 2 (October 24, 2019): 1622–42. http://dx.doi.org/10.1093/mnras/stz3003.

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ABSTRACT We use a set of 45 simulated clusters with a wide mass range (8 × 1013 < M500 [M⊙] < 2 × 1015) to investigate the effect of varying hydrodynamics flavours on cluster mass estimates. The cluster zooms were simulated using the same cosmological models as the BAHAMAS and C-EAGLE projects, leading to differences in both the hydrodynamics solvers and the subgrid physics but still producing clusters that broadly match observations. At the same mass resolution as BAHAMAS, for the most massive clusters (M500 > 1015 M⊙), we find that changes in the smoothed particle hydrodynamics (SPH) method produce the greatest differences in the final halo, while the subgrid models dominate at lower mass. By calculating the mass of all of the clusters using different permutations of the pressure, temperature, and density profiles, created with either the true simulated data or mock spectroscopic data, we find that the spectroscopic temperature causes a bias in the hydrostatic mass estimates that increases with the mass of the cluster, regardless of the SPH flavour used. For the most massive clusters, the estimated mass of the cluster using spectroscopic density and temperature profiles is found to be as low as 50 per cent of the true mass compared to ∼90 per cent for low-mass clusters. When including a correction for non-thermal pressure, the spectroscopic hydrostatic mass estimates are less biased on average and the mass dependence of the bias is reduced, although the scatter in the measurements does increase.

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Zhu, Ye, Kai Ming Ting, and Mark J. Carman. "Density-ratio based clustering for discovering clusters with varying densities." Pattern Recognition 60 (December 2016): 983–97. http://dx.doi.org/10.1016/j.patcog.2016.07.007.

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Haghi, Hosein, Ghasem Safaei, Akram H. Zonoozi, and Pavel Kroupa. "Dynamical evolution of star clusters with top-heavy IMF." Proceedings of the International Astronomical Union 14, S351 (May 2019): 447–50. http://dx.doi.org/10.1017/s174392131900704x.

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AbstractSeveral observational and theoretical studies suggest that the initial mass function (IMF) slope for massive stars in globular clusters (GCs) depends on the initial cloud density and metallicity, such that the IMF becomes increasingly top-heavy with decreasing metallicity and increasing the gas density of the forming object. Using N-body simulations of GCs starting with a top-heavy IMF and undergo early gas expulsion within a Milky Way-like potential, we show how such a cluster would evolve. By varying the degree of top-heaviness, we calculate the dissolution time and the minimum cluster mass needed for the cluster to survive after 12 Gyr of evolution.

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Частини книг з теми "DENSITY VARYING CLUSTERS"

Aarthi, B., Prathap Selvakumar, S. Subiksha, S. Chhavi, and Swetha Parathasarathy. "Comparative Analysis Implementation of Queuing Songs in Players Using Audio Clustering Algorithm." In Advances in Artificial and Human Intelligence in the Modern Era, 76–94. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-1301-5.ch004.

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This chapter compares the toughness of k-means, DBSCAN, and adaptive clustering algorithms for grouping data points into distinct clusters. The k-means algorithm is a widely used method that is easy to implement and efficient. The DBSCAN algorithm is a density-based method that is well-suited for datasets with clusters of varying densities, but it can be sensitive to the choice of parameters. In order to determine the ideal number of clusters within a dataset, adaptive clustering algorithms dynamically alter the number of clusters during the clustering process. The production of these algorithms is evaluated on a variety of datasets, and the results are compared in terms of accuracy and efficiency. According to the chapter's conclusion, each method has advantages and disadvantages of its own, and the ideal approach to apply will vary depending on the particular dataset and the objectives of the study.

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Savaş, Cihan, Mehmet Samet Yıldız, Süleyman Eken, Cevat İkibaş, and Ahmet Sayar. "Clustering Earthquake Data." In Big Data and Knowledge Sharing in Virtual Organizations, 224–39. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7519-1.ch010.

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Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.

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Smith, Patricia R., Keith M. Wilson, Ian E. G. Morrison, Richard J. Cherry,, and Nelson Fernandez. "Imaging of individual cell surface MHC antigens using fluorescent particles." In MHC, 133–52. Oxford University PressOxford, 1998. http://dx.doi.org/10.1093/oso/9780199635559.003.0007.

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Abstract Class I and class II major histocompatibility complex (MHC) molecules are cell surface, transmembrane glycoproteins that have similar overall structure but have readily distinguishable subunit structures (1). The two classes of MHC molecules present peptides, produced from different intracellular compartments, at the cell surface where they are recognized by two major subsets of T cells, CD4 and CDS, respectively. Integral membrane glycoproteins are able to diffuse to varying degrees within the plane of the membrane and so can potentially form protein clusters (2). This clustering of cell surface receptors in response to a particular signal or antibody is important in transmembrane signal transduction (3, 4). The density of class I and class II subsets and their relationship with other cell surface molecules in the correct orientation is thought to be vital for effective T cell recognition.

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Тези доповідей конференцій з теми "DENSITY VARYING CLUSTERS"

Khan, Mohammad Mahmudur Rahman, Md Abu Bakr Siddique, Rezoana Bente Arif, and Mahjabin Rahman Oishe. "ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities." In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT). IEEE, 2018. http://dx.doi.org/10.1109/ceeict.2018.8628138.

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Lee, Yong-Jiun, Poh-Seng Lee, and Siaw-Kiang Chou. "Hot Spot Mitigating With Oblique Finned Microchannel Heat Sink." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37817.

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Sectional oblique fins are employed in contrast to continuous fins in order to modulate the flow in microchannel heat sink. The breakage of continuous fin into oblique sections leads to re-initialization of boundary layers and generation of secondary flows which significantly enhance the cooling performance of the heat sink. In addition, oblique finned microchannel heat sink has the flexibility to tailor local heat transfer performance by varying its oblique fin pitch. Clusters of oblique fins at higher density can be created in order to promote greater degree of boundary layers redevelopment and secondary flows generation to provide more effective cooling at the high heat flux region. Thus the varying of oblique fin pitch can be exploited for hot spots mitigation. Simulation studies of silicon chip with hot spot shows more than 100% increment in local heat transfer coefficient at the high heat flux region for the variable pitch oblique finned microchannel compared with the conventional microchannel heat sink. Both the maximum temperature and its temperature gradient are reduced by 12.4°C as a result. Interestingly, there is only little or negligible pressure drop penalty associated with this novel heat transfer enhancement scheme in contrast to conventional enhancement techniques.

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Fronzeo, Melissa A., Michael Kinzel, and Jules Lindau. "Artificially Ventilated Cavities: Evaluating the Constant-Pressure Approximation." In ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69367.

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Computational Fluid Dynamics (CFD) is employed to study the fundamental aspects of the internal pressure within artificially ventilated, gaseous cavities in both twin- and toroidal-vortex closure modes. The results show that several pressure regions develop within the cavities, indicating that the common assumption that the cavity has a constant pressure breaks down when evaluated in high detail. The internal cavity pressure is evaluated using a probability density function (PDF). The resulting PDF plots show a clusters with multiple peaks. A mixture-of-Gaussians (MOG) method is employed to better understand the distributions of these peaks. These peaks are then mapped to the simulation results, where it is observed that these peaks correlate to distinct cavity regions (which vary depending on cavity type). Moreover, these varying pressure regions appear to align with cavity-radius growth and reduction and appear to be the driving force of the internal, circulatory flow. Lastly, the importance of these pressure regions are investigated with respect to predictions from semi-empirical theory of the cavity shape, showing a moderate impact depending on where the cavity is probed. Overall, these results provide physical insight into ventilated cavity flow behavior that is often ignored.

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Nejat, Amir, Pooya Mirzabeygi, and Masoud Shariat-Panahi. "Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88828.

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In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.

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Andrei, L., A. Andreini, C. Bianchini, B. Facchini, and L. Mazzei. "Numerical Analysis of Effusion Plates for Combustor Liners Cooling With Varying Density Ratio." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95039.

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Effusion cooling technology has been assessed in past years as one of the most efficient methods to maintain allowable working temperature of combustor liners. Despite many efforts reported in literature to characterize the cooling performances of those devices, detailed analysis of the mixing process between coolant and hot gas are difficult to perform especially in case superposition and density ratio effects become important. Furthermore, recent investigations on the acoustic properties of these perforations pointed out the challenge to maintain optimal cooling performance also with orthogonal holes which showed higher sound absorption. This paper performs a CFD analysis of the flow and thermal field associated with adiabatic wall conditions to compute the cooling effectiveness. The geometry consists of an effusion cooling plate drilled with 18 holes and fed separately with a cold and hot gas flow. Two types of perforations equivalent in porosity and pitches are investigated to assess the influence of the drilling angle between 30 and 90 deg. The reference conditions considered in this work comprehend an effective blowing ratio ranging between 1 and 3 at isothermal conditions (reaching a maximum hole Reynolds number of 10000) and high inlet turbulence intensity (17%). This set of conditions was exploited to perform a validation of the numerical procedure against detailed experimental data presented in another paper. Inlet turbulence effects highlighted by measurements for the slanted perforation were also investigated simulating a low turbulence condition corresponding to 1.6% of intensity. Furthermore the nominal DR = 1.0 was increased up to 1.7 to expand the available data set towards typical working conditions for aero-engines. Steady state RANS calculations were performed with the commercial code ANSYS® CFX, modeling turbulence by means of the k — ω SST. In order to include anisotropic diffusion effects due to turbulence damping in the near wall region, the turbulence model is corrected considering a tensorial definition of the eddy viscosity with an algebraic correction to dope its stream-span components. Computational grids were finely clustered close to the main plate and inside the holes to obtain y+ < 1, to maximize solver accuracy according to previous similar analysis.

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Bentosa, Elena, Glenn Makechnie, Jaime Rabines, and Yacine Meridji. "Drilling Parameter Based Geomechanical Log Application: A Fracture Stage Selection Case Study in Tight Gas Sand Development Wells." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22526-ea.

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Abstract In heterogeneous tight sand formations, horizontal wells encounter intervals deposited under varying depositional environments along the lateral portion of the wellbore between landing point and total depth. Horizontal wells in this study were drilled in tight sands deposited in a marine environment where lateral depositional facies changes are common, and hydraulic fracture stimulation is necessary to achieve economic hydrocarbon extraction due to the relatively low permeability of the formation. Without geomechanical logs currently derived from wireline logging, it is not possible to optimize cluster spacing and placement. This step provides necesary information used to optimize completion design, which is crucial to the ultimate productivity of a well. Due to formation heterogeneity, expensive wireline logs must be collected in order to optimize fracture stimulation or else new methods to estimate these logs must be employed. This paper presents a technique to optimize cluster selection for hydraulic fracturing in unconventional tight gas development horizontal wells without wireline logging by leveraging Measure While Drilling (MWD) Gamma Ray logs and surface drilling parameters together with Artificial Intelegence (AI) algorythms to predict density, compressional and shear slowness logs for use in geomechanical evaluation.

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Cai, Qingjun, Bing-chung Chen, Yuan Zhao, Julia Mack, Yanbao Ma, Chung-lung Chen, Hengzhi Wang, and Zhifeng Ren. "Thermal Property Measurements of Carbon Nanotube Forest Synthesized by Thermal CVD Process." In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88076.

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Carbon nanotube (CNT) forest/cluster synthesized by a thermal CVD process has millimeter growth height, large porosity and nano level pore size, plus high thermal conductivity of individual CNT, thus it is potentially a good wick structure material in developing micro heat transfer devices. However, thermal properties, including effective thermal conductivity (ETC) of a bulky CNT layer, may not be as good as the common metallic wick materials. In this paper, a Netzsch DSC 404 C Pegasus is used for measurement of the CNT heat capacity. CNT volume density is obtained by measuring the ratio of a bulky CNT weight and volume. Both the laser flash and 3-omega measurement methods are employed to measure ETC for CNT wick structures synthesized by the thermal CVD processes. For the laser flash method, measurement deviations caused by reflective silicon and thin substrate are corrected by surface treatment and increased sample thickness. Measurement results of the laser flash indicate that a 600μm thick CNT layer has ETC varying from 0.7–1.2W/m.K. For the 3-omega approach, the measurement system is validated on a quartz substrate. However, the test results yield larger ETC on 250μm CNT samples. Geometric and one dimensional thermal conduction analysis indicate that the bulky CNT thermal properties are tied to CNT synthesis processes. ETC of bulky CNT layer can be enhanced by straightening CNT growth and increasing CNT growth volume density.

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Gupta, Amit, and J. T. Kshirsagar. "Numerical Experiments on Solid Handling Pump." In ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/fedsm2006-98004.

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Solid handling pumps are used in managing sewage and other solid materials that could flow along with the water or any other fluid. The design of solid handling pump components is also deviating significantly from clear water pump components. The flow passages need to be maintained to allow solid particles of specified sizes smoothly without any obstruction. This aspect of different design from conventional design causes performance deterioration of the pump. The laboratory testing of the solid handling pumps is normally carried out with clear water and a judgment is used based on experience about the deterioration in performance caused by solid particles (size and number of particles). The actual performance measurement with varying particle sizes and its cluster density is very rarely available. A case of solid handling pump is taken up for detail investigation using numerical approach. The preliminary analysis has been carried out with solid handling impeller with clear water as medium of flow. The solid particles are then injected with different sizes and number of solid. Both Eulerian and Langrangian multiphase flow (MPF) approach have been used to study the hydraulic performance deterioration and particle trajectory in the working pump. The paper describes the approaches used and discusses the results considering generally accepted behavior of solid handing pumps.

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Ma, Jingsen, Chao-Tsung Hsiao, and Georges L. Chahine. "Euler-Lagrange Simulations of Bubble Cloud Dynamics Near a Wall." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65191.

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We present in this paper a two-way coupled Eulerian-Lagrangian model to study the dynamics of microbubble clouds exposed to incoming pressure waves and the resulting pressure loads on a nearby rigid wall. The model simulates the two-phase medium as a continuum and solves the N-S equations using Eulerian grids with a time and space varying density. The microbubbles are modeled as interacting spherical bubbles, which follow a modified Rayleigh-Plesset-Keller-Herring equation and are tracked in a Lagrangian fashion. A two-way coupling between the Euler and Lagrange components is realized through the local mixture density associated with the bubbles volume change and motion. Using this numerical framework, simulations involving a large number of bubbles were conducted under driving pressures of different frequencies. The results show that the frequency of the driving pressure is critical in determining the overall dynamics: either a collective strongly coupled cluster behavior or non-synchronized weaker multiple bubble oscillations. The former creates extremely high pressures with peak values orders of magnitudes higher than that of the excitation pressures. This occurs when the driving frequency matches the natural frequency of the bubble cloud. The initial distance between the bubble cloud and the wall is also critical on the resulting pressure loads. A bubble cloud collapsing very close to the wall exhibits a cascading collapse with the bubbles farthest from the wall collapsing first and the nearest ones collapsing last, thus the energy accumulates and then results in very violent pressure peaks at the wall. Farther from the wall, the bubble cloud collapses quasi spherically with the cloud center collapsing last.

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Cheng, W., K. Farhang, and Y. Kwon. "A Model for Contaminant Transport, Aggregation and Deposition in Lubricated Contact of Rough Surfaces." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81351.

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Effective prediction of lubricated contact poses an especially challenging problem. On the one hand surfaces are non-smooth at micron-scale while, on the other hand, a lubricant contains varying degrees of impurities in form of particle contaminants. An accurate model for lubricated contact, therefore, must be able to account for the various interactions that include particle-fluid, particle-particle, fluid-rough surface, particle-rough surface and rough surface-rough surface interactions. In the current study we propose the use of Lattice Boltzmann Method (LBM) and JKR theory for elasto-adhesive contact to simulate the particle transport processes in a lubricant flow between two rough surfaces. A particle dynamics simulation method is proposed to predict the interaction of a group of particles. The particle-particle and particle-boundary interactions are modeled by an extension of the JKR theory in which viscoelastic interactions are included through the implementation of the Kelvin-Voigt stress-strain relations. The particle-fluid interaction is calculated from the LBM simulation. Time scale relations between LBM and particle dynamics are characterized. Simulation results show the effects of particle density and surface roughness on frequencies of particle-particle and particle-boundary impacts, particle deposition rate, particle cluster forming and fluid boundary pressure changes due to particle deposition. A parametric study is performed to elucidate the effect of rough surface geometry and the relative velocity of the surfaces on friction and lift. The results provide fundamental insight on the contamination effects on wear and life of lubricated surfaces.

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