A Novel Principal Component Analysis-Based Acceleration Scheme for LES-ODT: An A Priori Study

A novel principal component analysis-based acceleration scheme for LES–ODT: An a priori study

A novel large-eddy simulation (LES) approach is developed for the study of turbulent non-premixed flames. The approach is based on the embedding of 1D flamelet solutions on the flame surface based the one-dimensional turbulence (ODT) model. LES is used to evolve mass conservation, momentum and a flame capturing scalar, the mixture fraction; while, ODT is used to solve for the momentum and thermo-chemical scalars (species and energy). The two solutions are implemented in parallel and coupled to account for the effects of turbulent on the flame and the effects of heat release on the flow. The ODT model resolution is designed to capture important fine-grained unresolved physics on the LES sub-filter scales, including finite-rate chemistry effects and the sub-filter scale flame wrinkling.

A framework for data-based turbulent combustion closure: A priori validation

In this project, a new methodology was proposed to bridge the gap between Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES). This novel methodology, titled Lattice-Based Multiscale Simulation (LBMS), creates a lattice structure of One-Dimensional Turbulence (ODT) models. This model has been shown to capture turbulent combustion with high fidelity by fully resolving interactions between turbulence and diffusion. By creating a lattice of ODT models, which are then coupled, LBMS overcomes the shortcomings of ODT, which are its inability to capture large scale three dimensional flow structures. However, by spacing these lattices significantly apart, LBMS can avoid the curse of dimensionality that creates untenable computational costs associated with DNS. This project has shown that LBMS is capable of reproducing statistics of isotropic turbulent flows while coarsening the spacing between lines significantly. It also investigates and resolves issues that arise when coupling ODT lines, such as flux reconstruction perpendicular to a given ODT line, preservation of conserved quantities when eddies cross a course cell volume and boundary condition application. Robust parallelization is also investigated.

One-dimensional turbulence (ODT) offers an alternative single-column solver for wall-bounded turbulence that sits between traditional 1D boundary-layer parametrisations and fully 3D direct numerical simulation: in fully resolved mode, molecular transport is explicitly resolved along a 1D vertical domain, while turbulent advection is represented by instantaneous spatial mappings ("eddy events"). For high-Reynolds-number, wall-bounded flows relevant to the atmospheric boundary layer (ABL), resolving Kolmogorov scales is prohibitively expensive. This motivates careful implementation of wall models and subgrid scale models in ODT similar to large eddy simulations (LES).Here, we develop an ODT formulation operated in an LES-like mode, in which unresolved eddy events are represented by a Smagorinsky–Lilly subgrid-scale (SGS) model, and surface coupling is provided through standard surface parametrisations for an extended range of resolved scales. We assess the formulation on two benchmark problems: (i) canonical smooth turbulent channel flow, using an algebraic wall model to supply surface stress consistent with resolved inertial-sublayer dynamics; and (ii) the GABLS1 intercomparison case (a weakly stable, shear-driven ABL with prescribed surface cooling rate), using Monin-Obukhov similarity theory to compute surface momentum and heat fluxes.Together, these two cases demonstrate the feasibility of combining ODT's eddy-event transport with LES-style SGS and surface models, thus providing a computationally efficient platform for future studies of ABL regimes, in which turbulence, surface fluxes, entrainment across sharp inversions, and multi-phase physics interact and remain challenging for coarse-resolution weather and climate models.

While time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) image generation has been useful in the determination of the spatial distribution of chemistry in a broad application area, the amount of secondary ion signal available in each pixel remains small, hampering the use of multivariate analysis (MVA) approaches. The pre‐treatment of data commonly comprises two approaches to increase pixel signal intensity prior to MVA: mass channel summation and pixel summation. Recent advances in instrumentation have lead to much greater signal per image pixel such that a true spectrum can be discerned at each point across the sample. In this study, we determine the outcomes of these two pre‐treatments prior to principal components analysis (PCA) and maximum autocorrelation factor (MAF) analysis in comparison with high signal intensity data. Both PCA and MAF analyses of the high signal intensity data are presented with MAF being identified as the most effective approach. Image data was reduced in intensity to determine the effectiveness of MVA with lower spectral intensity. MAF was found to outperform PCA on such data, although both techniques were useful in the identification of the chemistry present. The data were then mass summed, using a number of different approaches, to reduce mass resolution, leading to a detrimental effect on PCA analysis, but little discernable change to MAF output. Reducing the spatial resolution by summing spectra from adjacent pixels, however, lead to a severe blurring of the MAF image structure, with PCA performing well. Conclusions and recommendation for pre‐treatment and MVA technique selection are presented. Copyright © 2009 John Wiley & Sons, Ltd.

A filter-independent model identification technique for turbulent combustion modeling

Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents

This paper proposes a fast recursive PCA (principal component analysis) algorithm to remove face occlusions. In training phase, all faces are normalized by two eye centers and two mouth corners, and eigenvectors (eigenfaces) were obtained by PCA analysis. In test phase, face occlusion is removed by iteratively perform two steps of analysis and synthesis. New damaged face is first normalized by clicking four feature points, and PCA coefficients are obtained in analysis step. In synthesis step, reconstructed face is obtained by linear combining eigenfaces, and coefficients error between two successive analyses is used for fast PCA compensation. Experimental results on training and test faces show that the proposed algorithm convergences faster than classical PCA compensation and reconstructed faces are natural.

The combination of advanced techniques with multivariate analysis methods are being used with great confidence by forensics experts due to their high precision and accuracy. The prime objective of present work was to explore the potential of chemometric tools in discriminating and predicting the blue pen inks when applied on Attenuated total reflectance Fourier transform infrared (ATR-FTIR) data of pen strokes of different brands and models commercially available in Pakistani markets. The pen strokes on fine A4 paper and recycled A4 paper were obtained from 86 samples including ballpoints, gel, oil- gel and roller ball pens. ATR-FTIR spectra were first characterized for different functional groups present in ink pigments however, they were apparently indistinguishable having some common functional groups. After spectra preprocessing the principal component analysis (PCA) and agglomerative hierarchical cluster analysis (AHC) showed that pen strokes could be distinguished. Both PCA and AHC analyses showed that ballpoints and gel pen could be discriminated and grouped separately. PCA analyses also showed the discrimination between local and foreign brands of ballpoints. Finally, classification method partial least squares-discriminant analysis (PLS-DA) was successfully applied for the classification and subsequently for the identification of type of pen used in personnel’s signature to mimic a forensic case. To evaluate PLS-DA classification performance, the classification parameters such as sensitivity, specificity, accuracy and precision were also derived from confusion matrix. Overall, this work emphasized the worth of multivariate analysis in classification and prediction of type of writing pen in a document as a forensic study.

A rapid and accurate method of UFLC-Q-TOF-MS/MS combined with multivariate statistical analysis was established for the identification of Ainsliaea fragrans from different origins in this study. The A. fragrans from different producing areas of Jiangxi, Yunnan, Henan and Jiangsu were determined by UFLC-Q-TOF-MS/MS in the negative ion mode. And the data of the study were analyzed by the Markerview and other software for the PCA and OPLS-DA cluster analysis as well as t test. The results of the principal component analysis(PCA)showed that the main components from different origins were well distinguished. And the results of multivariate statistical showed the differences and similarities between different producing areas. Besides, 40 different compounds were identified in the negative ion mode. This method for identifying A. fragrans from different producing areas has the advantages of rapid accuracy and simplicity, which laid the foundation for the evaluation of the quality of the A. fragrans.

Event Abstract Back to Event Standardization of catch-per-unit-effort in a multi-gear multi-species fishery Lorena Couce1*, Angelo Santana2, Alberto Bilbao-Sieyro1, Yeray Perez1 and Jose J. Castro3 1 Management of the Rural Environment of Canarias SAU, Spain 2 University of Las Palmas de Gran Canaria, Departamento de Matemáticas, Spain 3 Instituto Universitario de Acuicultura Sostenible y Ecosistemas Marinos (IU-ECOAQUA), Spain Catch-per-unit-effort (CPUE) data are commonly standardized taking into account factors that influence catch rates that are not related to resource abundance. In this work we analyze catch rates and effort data of a small-scale fishery targeting multiple species in the Canary Islands. We developed a method that combines principal component scores derived from a Principal Component Analysis (PCA) of the effort data within a Generalized Additive Model (GAM) framework. From the PCA analysis we also estimated the relative fishing power of each vessel with reference to a standard vessel for which the principal components are equal to zero, and based on the results we define two categories; the first category includes the benthic and demersal species and the second category includes the oceanic and pelagic species. The GAM model described 66.1% of the total deviance and includes seasonal variables, fishing grounds, climate index, oceanographic and biological parameters and dummies for tuna season and medium-sized coastal pelagics. The variables with the highest contribution in order of importance were the tuna dummy, the first principal component, the medium-sized coastal pelagics dummy and the fishing ground. The standardized CPUE time series showed a slight positive trend throughout the analyzed period as well as a marked seasonal component due to the tuna harvest. However, when the CPUE is analyzed according to the target groups, a negative trend is observed in the herviborous and invertebrate feeders group, reef-associated fishes, shallow-water demersals and molluscs from 2010, and the benthic cephalopods show an appreciable downward trend from 2012. We consider the approach presented here has potentially useful for stock assessment in similar multi-species fisheries. Acknowledgements This work was developed within the framework of the PLASMAR Project (MAC/1.1a/030), with the support of the European Union (EU) and co-financed by the European Regional Development Fund (EDRF ) and the INTERREG V-A Spain-Portugal MAC 2014-2020 (Madeira-Azores-Canarias). Keywords: Small-scale fishery, GAM (generalized additive model), PCA principal component analysis, CPUE, standardization, Multi-gear fisheries, Multi-species fisheries Conference: XX Iberian Symposium on Marine Biology Studies (SIEBM XX) , Braga, Portugal, 9 Sep - 12 Sep, 2019. Presentation Type: Poster Presentation Topic: Fisheries, Aquaculture and Biotechnology Citation: Couce L, Santana A, Bilbao-Sieyro A, Perez Y and Castro J (2019). Standardization of catch-per-unit-effort in a multi-gear multi-species fishery. Front. Mar. Sci. Conference Abstract: XX Iberian Symposium on Marine Biology Studies (SIEBM XX) . doi: 10.3389/conf.fmars.2019.08.00161 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Jun 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Lorena Couce, Management of the Rural Environment of Canarias SAU, Agüimes, Spain, lorenacm@gmrcanarias.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lorena Couce Angelo Santana Alberto Bilbao-Sieyro Yeray Perez Jose J. Castro Google Lorena Couce Angelo Santana Alberto Bilbao-Sieyro Yeray Perez Jose J. Castro Google Scholar Lorena Couce Angelo Santana Alberto Bilbao-Sieyro Yeray Perez Jose J. Castro PubMed Lorena Couce Angelo Santana Alberto Bilbao-Sieyro Yeray Perez Jose J. Castro Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

In recent year, stock investor in Indonesia increased rapidly, so it is required to do analysis about the stock that helps the investor in their investment plan. Clustering is beneficial to select the appropriate stock for investors. Unfortunately, stock prices keep varying from time to time. Consequently, it is not an easy work to select the stock for investment. In addition, stock price time series data are high dimensional data that influenced by many factors. In this study, high dimensional data are obtained by the time frame of each factor. Therefore, it is important to use a suitable technique to cluster high dimensional data. This paper presents High Dimensional Data Clustering (HDDC), a model-based clustering based on Gaussian Mixture Model, using the Expectation-Maximization (EM) algorithm. HDDC via EM algorithm gives a more robust result, and it possible to make an additional assumption. Moreover, this paper combines a high-dimensional clustering technique HDDC via EM algorithm and the most popular feature extraction technique Principal Component Analysis (PCA). This paper comparing methods of clustering technique HDDC and the combination between HDDC and PCA to know the most effective method which gives better result in clustering high-dimensional time series data. The 155 data features are reduced to 7 principal components using PCA analysis. Despite PCA has increased the time efficiency of building the model, clustering technique HDDC via EM algorithm enables to handle the high-dimensional data better than the combination with PCA.

The water quality assessment for wastewater reclamation is usually related to many parameters. This would introduce difficulty in objectively and comprehensively evaluating the overall water quality, and affect the water reuse safety. To address this problem, a principal component analysis (PCA) method was employed in this study to identify the components that mainly affect water quality. By examining eleven indices, a large amount of water quality data of four typical wastewater reclamation treatment plants (WRTP) in northern China were collected. The PCA results indicated that four uncorrelated principal components (PC) could represent the majority of water quality information. The first PC was mainly involved with the COD index and the sensory properties of water, including chroma, turbidity, dissolved solids, and total hardness. The second PC included the indices of NH3-N, DBP (di-n-butyl phthalate), and DEHP (Di (2-ethylhexyl) phthalate), reflecting the water reuse safety on both human health and ecological environment. The total phosphorus (TP) and iron (Fe) were included in the third PC, while the fourth PC included dissolved oxygen (DO). Based on PCA analysis, a comprehensive score of water quality was also obtained for each WRTP during each representative month, and it was found that the wastewater reclamation process could greatly affect the quality of reclaimed water. These four identified principle components can help evaluate the overall water quality, and much attention should be given to their effective monitoring and control during the operation of WRTP or the selection of appropriate treatment process.

Principal Component Analysis (PCA) is one of the most popular dimensionality-reduction techniques used for the purpose of classification. It has gained popularity because of its ease of use and optimal nature in a mean square error sense. This paper focuses on an analysis of PCA as a classification tool for rainfall images. Rainfall maps were generated using monthly precipitation data from 226 stations over Mauritius for the years 1992 to 1995 and also for the mean monthly rainfall for the years 1961 to 1990. Three different methods of applying PCA were then used on the rainfall maps and each method generated principal components reflecting the relevant percentage of variability. To interpret these components, a study of the various rainfall bearing climate systems and the wind-field active in Mauritius proved to be necessary to enable their association of the most significant principal components. The effect of altitude on rainfall has been found to have most dominant effect on rainfall. Other climate systems such as the ITCZ, cyclones, anticyclones, cold fronts and the perturbation of easterlies were also found to have specific rainfall patterns associated with them.