Relevant bibliographies by topics / Dimensional analysis

Academic literature on the topic 'Dimensional analysis'

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Published: 4 June 2021
Last updated: 1 August 2024

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Journal articles on the topic "Dimensional analysis"

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Dobáková, Romana, Natália Jasminská, Tomáš Brestovič, Mária Čarnogurská, and Marián Lázár. "Dimensional analysis application when calculating heat losses." International Journal of Engineering Research and Science 3, no. 9 (September 30, 2017): 29–34. http://dx.doi.org/10.25125/engineering-journal-ijoer-sep-2017-5.

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Cookson, Kristine L. "Dimensional analysis." Nursing 43, no. 6 (June 2013): 57–62. http://dx.doi.org/10.1097/01.nurse.0000428696.87216.e1.

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Buchanan, Mark. "Dimensional analysis." Nature Physics 6, no. 8 (August 2010): 555. http://dx.doi.org/10.1038/nphys1744.

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Mahoney, John F., and Sencer Yeralan. "Dimensional Analysis." Procedia Manufacturing 38 (2019): 694–701. http://dx.doi.org/10.1016/j.promfg.2020.01.094.

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Rao, N. N. "Dimensional analysis." Resonance 1, no. 11 (November 1996): 29–41. http://dx.doi.org/10.1007/bf02835211.

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Fei, Teng, Bin Guo, and Duong H. Phong. "Parabolic dimensional reductions of 11-dimensional supergravity." Analysis & PDE 14, no. 5 (August 22, 2021): 1333–61. http://dx.doi.org/10.2140/apde.2021.14.1333.

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SINGH, KAMAL BHAN, and YOGESH MISHRA. "TWO DIMENSIONAL AEROFOIL WIND TURBINE BLADE DESIGN AND ANALYSIS USING CFD ANALYSIS." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 9, no. 4 (December 31, 2021): 25–27. http://dx.doi.org/10.55083/irjeas.2021.v09i04005.

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In the current scenario, wind turbine energy accounts for 70% of total renewable energy used in India. The Indian wind energy sector has a capacity of 20 GW installed (as on 31.5.2017). In terms of installed wind power capacity, India ranks fifth in the world and is regarded as a major player in the global wind energy market. For the current work, a design method based on modelling in Uni-graphics and followed by simulation using the CFD (Computational Fluid Dynamics) star ccm + programme is chosen, so that the expense and time required to find the optimum aerodynamic design of a wind turbine blade by experiment can be minimised.
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Campanelli, Leonardo. "Dimensional analysis of two-dimensional turbulence." Modern Physics Letters B 33, no. 19 (July 8, 2019): 1950218. http://dx.doi.org/10.1142/s021798491950218x.

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We study the scaling properties of two-dimensional turbulence using dimensional analysis. In particular, we consider the energy spectrum both at large and small scales and in the “inertial ranges” for the cases of freely decaying and forced turbulence. We also investigate the properties of an “energy condensate” at large scales in spatially finite systems. Finally, an analysis of a possible inverse cascade in freely decaying turbulence is presented.
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Butterfield, R. "Dimensional analysis revisited." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 215, no. 11 (November 1, 2001): 1365–75. http://dx.doi.org/10.1243/0954406011524748.

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Dimensionless groups, the output of a successful dimensional analysis, are usually developed via Buckingham's pi theorem. Because this theorem provides a necessary but not sufficient condition for a solution, such a dimensional analysis may, on occasion, appear to fail. The paper presents the necessary and sufficient conditions in a simple form and builds on them to demonstrate how new physical knowledge can augment a ‘primitive’ set of dimensions to arrive at an optimal number of dimensionless groups. The formulation is used to elucidate the historical Rayleigh-Ria-bouchinsky controversy and a related thermomechanical problem is analysed to demonstrate the complete ‘new knowledge’ algorithm. Mathematica code is appended which incorporates these ideas and generates the complete set of admissible dimensionless groups for any specific problem.
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Gonzalez, Dean W., and Tim Peart. "Applying dimensional analysis." ACM SIGAda Ada Letters XIII, no. 4 (July 1993): 77–86. http://dx.doi.org/10.1145/163105.163113.

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Dissertations / Theses on the topic "Dimensional analysis"

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Wang, Tao. "Variable selection and dimension reduction in high-dimensional regression." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1544.

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Aljarrah, Inad A. "Three Dimensional Face Recognition Using Two Dimensional Principal Component Analysis." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1142453613.

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Long, Xianbin. "Dimensional analysis and partial evaluation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq23390.pdf.

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Hubble, Andrew. "Two-Dimensional Compressible Vortex Analysis." Thesis, Tufts University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1589459.

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With the introduction of bivelocity in 2004, new models for the prediction of Maxwellian, compressible micro-channel flows have recently been developed. This thesis utilizes a non-kinetic method to explore an analytic prediction of two-dimensional velocity profiles of steady and transient, unbounded, compressible vortex flows. This bivelocity hydrodynamic model is compared to the traditional analytical solutions of the Navier-Stokes Fourier (NSF), Boltzmann, and Burnett equations, as well as to Mandella's 1987 experiments in compressible vorticies, highlighting the stark contrasts between these models. While definitive convergence has yet to be obtained, the bivelocity approach has shown promise in resolving these issues. The importance of these bivelocity addition provides a clean explanation for the poor analytic correlations to experimental data previously attempted. While further investigation is required, bivelocity definitively improves upon the current models..

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Battey, Heather Suzanne. "Dimension reduction and automatic smoothing in high dimensional and functional data analysis." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609849.

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Kanneganti, Raghuveer. "CLASSIFICATION OF ONE-DIMENSIONAL AND TWO-DIMENSIONAL SIGNALS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/892.

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This dissertation focuses on the classification of one-dimensional and two-dimensional signals. The one-dimensional signal classification problem involves the classification of brain signals for identifying the emotional responses of human subjects under given drug conditions. A strategy is developed to accurately classify ERPs in order to identify human emotions based on brain reactivity to emotional, neutral, and cigarette-related stimuli in smokers. A multichannel spatio-temporal model is employed to overcome the curse of dimensionality that plagues the design of parametric multivariate classifiers for multi-channel ERPs. The strategy is tested on the ERPs of 156 smokers who participated in a smoking cessation program. One half of the subjects were given nicotine patches and the other half were given placebo patches. ERPs were collected from 29 channel in response to the presentation of the pictures with emotional (pleasant and unpleasant), neutral/boring, and cigarette-related content. It is shown that human emotions can be classified accurately and the results also show that smoking cessation causes a drop in the classification accuracies of emotions in the placebo group, but not in the nicotine patch group. Given that individual brain patterns were compared with group average brain patterns, the findings support the view that individuals tend to have similar brain reactions to different types of emotional stimuli. Overall, this new classification approach to identify differential brain responses to different emotional types could lead to new knowledge concerning brain mechanisms associated with emotions common to most or all people. This novel classification technique for identifying emotions in the present study suggests that smoking cessation without nicotine replacement results in poorer differentiation of brain responses to different emotional stimuli. Future, directions in this area would be to use these methods to assess individual differences in responses to emotional stimuli and to different drug treatments. Advantages of this and other brain-based assessment include temporal precision (e.g, 400-800 ms post stimulus), and the elimination of biases related to self-report measures. The two-dimensional signal classification problems include the detection of graphite in testing documents and the detection of fraudulent bubbles in test sheets. A strategy is developed to detect graphite responses in optical mark recognition (OMR) documents using inexpensive visible light scanners. The main challenge in the formulation of the strategy is that the detection should be invariant to the numerous background colors and artwork in typical optical mark recognition documents. A test document is modeled as a superposition of a graphite response image and a background image. The background image in turn is modeled as superposition of screening artwork, lines, and machine text components. A sequence of image processing operations and a pattern recognition algorithm are developed to estimate the graphite response image from a test document by systematically removing the components of the background image. The proposed strategy is tested on a wide range of scanned documents and it is shown that the estimated graphite response images are visually similar to those scanned by very expensive infra-red scanners currently employed for optical mark recognition. The robustness of the detection strategy is also demonstrated by testing a large number of simulated test documents. A procedure is also developed to autonomously determine if cheating has occurred by detecting the presence of aberrant responses in scanned OMR test books. The challenges introduced by the significant imbalance in the numbers of typical and aberrant bubbles were identified. The aberrant bubble detection problem is formulated as an outlier detection problem. A feature based outlier detection procedure in conjunction with a one-class SVM classifier is developed. A multi-criteria rank-of-rank-sum technique is introduced to rank and select a subset of features from a pool of candidate features. Using the data set of 11 individuals, it is shown that a detection accuracy of over 90% is possible. Experiments conducted on three real test books flagged for suspected cheating showed that the proposed strategy has the potential to be deployed in practice.
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Ruan, Lingyan. "Statistical analysis of high dimensional data." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37135.

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This century is surely the century of data (Donoho, 2000). Data analysis has been an emerging activity over the last few decades. High dimensional data is in particular more and more pervasive with the advance of massive data collection system, such as microarrays, satellite imagery, and financial data. However, analysis of high dimensional data is of challenge with the so called curse of dimensionality (Bellman 1961). This research dissertation presents several methodologies in the application of high dimensional data analysis. The first part discusses a joint analysis of multiple microarray gene expressions. Microarray analysis dates back to Golub et al. (1999). It draws much attention after that. One common goal of microarray analysis is to determine which genes are differentially expressed. These genes behave significantly differently between groups of individuals. However, in microarray analysis, there are thousands of genes but few arrays (samples, individuals) and thus relatively low reproducibility remains. It is natural to consider joint analyses that could combine microarrays from different experiments effectively in order to achieve improved accuracy. In particular, we present a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice. The second part is about covariance matrix estimation in high dimensional data. First, we propose a penalised likelihood estimator for high dimensional t-distribution. The student t-distribution is of increasing interest in mathematical finance, education and many other applications. However, the application in t-distribution is limited by the difficulty in the parameter estimation of the covariance matrix for high dimensional data. We show that by imposing LASSO penalty on the Cholesky factors of the covariance matrix, EM algorithm can efficiently compute the estimator and it performs much better than other popular estimators. Secondly, we propose an estimator for high dimensional Gaussian mixture models. Finite Gaussian mixture models are widely used in statistics thanks to its great flexibility. However, parameter estimation for Gaussian mixture models with high dimensionality can be rather challenging because of the huge number of parameters that need to be estimated. For such purposes, we propose a penalized likelihood estimator to specifically address such difficulties. The LASSO penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps reducing the dimensionality of the problem. We show that the proposed estimator can be efficiently computed via an Expectation-Maximization algorithm. To illustrate the practical merits of the proposed method, we consider its application in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool in handling high dimensional data. Finally, we present structured estimators for high dimensional Gaussian mixture models. The graphical representation of every cluster in Gaussian mixture models may have the same or similar structure, which is an important feature in many applications, such as image processing, speech recognition and gene network analysis. Failure to consider the sharing structure would deteriorate the estimation accuracy. To address such issues, we propose two structured estimators, hierarchical Lasso estimator and group Lasso estimator. An EM algorithm can be applied to conveniently solve the estimation problem. We show that when clusters share similar structures, the proposed estimator perform much better than the separate Lasso estimator.
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Shen, Xilin. "Multiscale analysis of high dimensional data." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284443.

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Ferrecchia, Antonella. "Analysis of three-dimensional dynamic stall." Thesis, University of Glasgow, 2002. http://theses.gla.ac.uk/4429/.

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The work presented in this thesis attempts to provide a deeper understanding of the physical phenomena associated with the dynamic stall process on finite wing planforms. The work involves the analysis of data from the Glasgow University unsteady aerodynamics database that has been built up over a number of years through contributions from a range of researchers. Analysis focuses on two finite wing models; one a rectangular wing of aspect ratio three and the other with the same overall dimensions but with 60o swept tips. However, as most research to date has focused on nominally two-dimensional data, the results are referenced to measurements made on a nominally two-dimensional NACA 0015 aerofoil model. This is appropriate as this aerofoil was used as the wing section of both of the three-dimensional wing models. Flow visualisation images collected in a previous study also provide valuable information to supplement the pressure analysis. It is shown that, although the flow at the mid span sections of the finite wings exhibit many of the features of the two-dimensional case, there are some significant differences. In particular, the three-dimensional flow is dominated by the downwash from the wing tips. This causes the normal force response during pitching to lag the static normal force curve. This is in complete contrast to the two-dimensional case where the shed vorticity induces the opposite effect. The downwash also influences the incidence of lift stall but it does so in a manner that is dependent on the reduced pitch rate. Despite these effects, it is established that the flow behaviour in the mid-span region is almost two-dimensional prior to vortex inception. This provides an opportunity to examine the relationship between the generation of vorticity, or vorticity flux, in the leading edge region and the origins of the dynamic stall vortex at specific span locations in location. The vorticity flux distributions around the leading edges of the nominally two-dimensional NACA 0015 aerofoil and the two finite wings are then examined for pitching cases. On this basis a link is established between the peak vorticity flux and the dynamic stall vortex formation. This is confirmed by comparison of the vorticity flux measurements with a previous dynamic stall vortex detection method. The two methods are shown to five almost identical results in situations where the flow may be considered nominally two-dimensional. This suggests that monitoring vorticity flux may provide a practical method of dynamic stall vortex detection. In regions of the finite wings that exhibit strong three-dimensional flow effects, i.e. away from the mid-span, the peak vorticity flux is achieved after the dynamic stall vortex forms. This suggests that vortex formation is triggered by interference from adjacent sections of the wing. To examine this possibility, the vorticity flux is compared to a criterion used to detect the initial instability of the boundary layer at the leading edge. It is shown that the relationship between this criterion and the peak vorticity flux is the same along the span of the wing. This is a significant result as it demonstrates that, although the leading edge response determines the incidence of vortex onset near the mid-span, the formation of the vortex on sections of the wing closer to the tips occurs before the leading edge becomes critical. The implications of this for dynamic stall modelling of two-dimensional dynamic stall predictors with lifting line formulations will not capture this effect.
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Donaghy, Richard James. "Dimensional reduction of stress analysis models." Thesis, Queen's University Belfast, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263368.

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Books on the topic "Dimensional analysis"

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Gibbings, J. C. Dimensional Analysis. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6.

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Tan, Qing-Ming. Dimensional Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19234-0.

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Gibbings, J. C. Dimensional analysis. London: Springer, 2011.

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Barenblatt, G. I. Dimensional analysis. New York: Gordon and Breach Science Publishers, 1987.

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Aliprantis, Charalambos D., and Kim C. Border. Infinite Dimensional Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-03004-2.

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Aliprantis, Charalambos D., and Kim C. Border. Infinite Dimensional Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03961-8.

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Conejo, Alberto N. Fundamentals of Dimensional Analysis. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1602-0.

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Garello, René, ed. Two-Dimensional Signal Analysis. London, UK: ISTE, 2008. http://dx.doi.org/10.1002/9780470611067.

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Simon, Volker, Bernhard Weigand, and Hassan Gomaa. Dimensional Analysis for Engineers. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52028-5.

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Curren, Anna M. Dimensional analysis for meds. 4th ed. Australia: Delmar Cengage Learning, 2010.

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Book chapters on the topic "Dimensional analysis"

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Gibbings, J. C. "An Elementary Introduction." In Dimensional Analysis, 1–23. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_1.

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Gibbings, J. C. "Assessing Experimental Correlations." In Dimensional Analysis, 255–71. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_10.

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Gibbings, J. C. "Similar Systems." In Dimensional Analysis, 273–84. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_11.

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Gibbings, J. C. "Concepts, Dimensions and Units." In Dimensional Analysis, 25–54. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_2.

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Gibbings, J. C. "The Pi-Theorem." In Dimensional Analysis, 55–82. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_3.

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Gibbings, J. C. "The Development of Dimensional Analysis." In Dimensional Analysis, 83–93. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_4.

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Gibbings, J. C. "The Choice of Dimensions." In Dimensional Analysis, 95–114. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_5.

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Gibbings, J. C. "Supplementation of Derivations." In Dimensional Analysis, 115–47. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_6.

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Gibbings, J. C. "Systematic Experiment." In Dimensional Analysis, 149–77. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_7.

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Gibbings, J. C. "Analytical Results." In Dimensional Analysis, 179–218. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-317-6_8.

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Conference papers on the topic "Dimensional analysis"

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Radojicic, Una, Niko Lictzen, Klaus Nordhausen, and Joni Virta. "Dimension Estimation in Two-Dimensional PCA." In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2021. http://dx.doi.org/10.1109/ispa52656.2021.9552114.

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Barhoumi, Abdessatar, Habib Ouerdiane, and Anis Riahi. "Infinite dimensional Gegenbauer functionals." In Noncommutative Harmonic Analysis with Applications to Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2007. http://dx.doi.org/10.4064/bc78-0-2.

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Ogata, Hidenori. "Complex Variable Boundary Element Method for Two‐Dimensional Potential Problems with One‐Dimensional Periodicity." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990949.

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Asada, Akira. "Zeta-regularization and calculus on infinite dimensional spaces." In GLOBAL ANALYSIS AND APPLIED MATHEMATICS: International Workshop on Global Analysis. AIP, 2004. http://dx.doi.org/10.1063/1.1814716.

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Yoshida, Shuhei, and Manabu Yamamoto. "Analysis of photopolymer’s hologram recording characteristics." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/dh.2007.dtub6.

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Lee, Kwang-Hoon, Dong-Wook Kim, Yong-Moo Kwon, Nam-Ho Hur, and Sung-Kyu Kim. "CORRECT DEPTH ANALYSIS ON THE STEREOSCOPY." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/dh.2009.dwb21.

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Özay, Evrim Korkmaz, Metin Demiralp, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Piecewise Vector High Dimensional Model Representation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637818.

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Tang, Jianguo, Shuming Gao, Ming Li, and Feiwei Qin. "Mixed-Dimensional Model Analysis Under Dimension Reduction Error Control." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70576.

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In order to conduct engineering analysis efficiently, complex CAD model is generally idealized by dimension reduction of its local thin regions into mid-surfaces, which results in a mixed-dimensional model. However, such dimension reduction inevitably induces analysis errors when plate or shell theory applied to the mixed-dimensional model. In this paper, an evaluation indicator is proposed for estimating analysis error induced by dimension reduction of a original model into mixed-dimensional model and used to control the analysis results of the mixed-dimensional model with given accuracy. The evaluation indicator is defined as the stress difference on the coupling interface between the mixed-dimensional model and the original model. When the mixed-dimensional model is analyzed, p-version solid elements were generated by offsetting the shell nodes in the thickness direction. Moreover, element stiffness matrix, boundary conditions and material properties can be extracted from the analysis results and reused for the indicator computation. Displacements of the mixed-dimensional model are input as initial value to iterative solver to accelerate the computation. When the indicator is below the accuracy, final analysis can be proceeded with p-adaptivity in the thin regions. The hierarchical shape function for p-version solid elements ensures the efficiency of the error estimation and the reliability of the final analysis. The robustness of the evaluation indicator and computational efficiency for final analysis are illustrated by experiments on engineering models.
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Gadepally, Vijay, and Jeremy Kepner. "Big data dimensional analysis." In 2014 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2014. http://dx.doi.org/10.1109/hpec.2014.7040944.

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Soroka, William L., Thomas J. Fitch, Kirk H. Van Sickle, and Phil D. North. "Three‐dimensional AVO analysis." In SEG Technical Program Expanded Abstracts 1990. Society of Exploration Geophysicists, 1990. http://dx.doi.org/10.1190/1.1890046.

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Reports on the topic "Dimensional analysis"

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Evans, John, Simon Frechette, John Horst, Hui Huang, Thomas Kramer, Elena Messina, Fred Proctor, et al. Analysis of dimensional metrology standards. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6847.

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Loehle, C. S. An algorithm for symbolic dimensional analysis. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6357117.

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Chan, Y. T., R. C. Buggeln, and H. McDonald. Three-Dimensional Dynamic Labyrinth Seal Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada185353.

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Ellison, Steven. A Two-Dimensional Plasma Actuator Analysis Code. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada442779.

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Guttinger, Kimberly. Multi-Dimensional Analysis of Fluorescent Chemosensor Data. Portland State University Library, January 2015. http://dx.doi.org/10.15760/honors.194.

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Herman, Matthew Joseph. Two-Dimensional Correlation Method for Polymer Analysis. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1172208.

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Misra, Vedant, Dion Harmon, Blake Stacey, Joseph Ornstein, and Yaneer Bar-Yam. Vulnerability Analysis of High Dimensional Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada535621.

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Jin, Victoria Y., and Alexander H. Levis. Command and Control Experiment Design Using Dimensional Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197933.

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Baraniuk, Richard G., and Hyeokho Choi. Higher-Dimensional Signal Processing via Multiscale Geometric Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada514181.

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10

Valdez, Lucas M. Uncertainty Budget Analysis for Dimensional Inspection Processes (U). Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1047126.

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