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Journal articles on the topic 'Geometrical uncertainties'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Stroom, Joep. "Safety margins for geometrical uncertainties in radiotherapy." Medical Physics 27, no. 9 (September 2000): 2194. http://dx.doi.org/10.1118/1.1289899.

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2

Schwarz, M. "SP-0199: Geometrical uncertainties and proton treatment planning." Radiotherapy and Oncology 111 (2014): S79. http://dx.doi.org/10.1016/s0167-8140(15)30304-2.

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3

Mailhe, J., J. M. Linares, J. M. Sprauel, and P. Bourdet. "Geometrical checking by virtual gauge, including measurement uncertainties." CIRP Annals 57, no. 1 (2008): 513–16. http://dx.doi.org/10.1016/j.cirp.2008.03.112.

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4

Mileusnic, Dusan. "Verification and correction of geometrical uncertainties in conformal radiotherapy." Archive of Oncology 13, no. 3-4 (2005): 140–44. http://dx.doi.org/10.2298/aoo0504140m.

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Geometrical errors are presented as deviation between intended geometry of radiotherapy plan and real geometry of radiotherapy treatment. Total geometrical error is build up of smaller errors, which can be generally classified as set-up, organ motion, organ delineation, and technical condition related errors. The clear distinction must be made between systematic and random component of these errors and its amount should be encountered in treatment planning process. Errors? measuring for specific patient group with electronic portal imaging device and proper correction strategy enables to predict, minimize, and keep under control the amount for most of geometrical errors; it also improves the preciseness of treatment and consequent results. Nature and characteristics of most frequent geometrical errors are discussed and clinically applicable methods for their proper managing are described in this paper.
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5

Agarwal, Nitin, and N. R. Aluru. "A stochastic Lagrangian approach for geometrical uncertainties in electrostatics." Journal of Computational Physics 226, no. 1 (September 2007): 156–79. http://dx.doi.org/10.1016/j.jcp.2007.03.026.

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6

Li, Zhao Kun, Hua Mei Bian, Li Juan Shi, and Xiao Tie Niu. "Reliability-Based Topology Optimization of Compliant Mechanisms with Geometrically Nonlinearity." Applied Mechanics and Materials 556-562 (May 2014): 4422–34. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4422.

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A new reliability-based topology optimization method for compliant mechanisms with geometrical nonlinearity is presented. The aim of this paper is to integrate reliability and geometrical nonlinear analysis into the topology optimization problems. Firstly, geometrical nonlinear response analysis method of the compliant mechanisms is developed based on the Total-Lagrange finite element formulation, the incremental scheme and the Newton-Raphson iteration method. Secondly, a multi-objective topology optimal model of compliant mechanisms considering the uncertainties of the applied loads and the geometry descriptions is established. The objective function is defined by minimum the compliance and maximum the geometric advantage to meet both the stiffness and the flexibility requirements, and the reliabilities of the compliant mechanisms are evaluated by using the first order reliability method. Thirdly, the computation of the sensitivities is developed with the adjoint method and the optimization problem is solved by using the Method of Moving Asymptotes. Finally, through numerical calculations, reliability-based topology designs with geometric nonlinearity of a typical compliant micro-gripper and a multi-input and multi-output compliant sage are obtained. The importance of considering uncertainties and geometric nonlinearity is then demonstrated by comparing the results obtained by the proposed method with deterministic optimal designs, which shows that the reliability-based topology optimization yields mechanisms that are more reliable than those produced by deterministic topology optimization.
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7

Remeijer, P. "MANAGEMENT OF GEOMETRICAL UNCERTAINTIES IN RADIOTHERAPY: MARGINS AND CORRECTION STRATEGIES." Radiotherapy and Oncology 92 (August 2009): S124. http://dx.doi.org/10.1016/s0167-8140(12)72915-8.

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8

Stroom, Joep C., and Ben J. M. Heijmen. "Geometrical uncertainties, radiotherapy planning margins, and the ICRU-62 report." Radiotherapy and Oncology 64, no. 1 (July 2002): 75–83. http://dx.doi.org/10.1016/s0167-8140(02)00140-8.

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9

Bel, A., M. van Herk, and J. V. Lebesque. "Target margins for random geometrical treatment uncertainties in conformal radiotherapy." Medical Physics 23, no. 9 (September 1996): 1537–45. http://dx.doi.org/10.1118/1.597745.

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10

MATSUMOTO, Masahide, and Etsuo IWAYA. "Interval Finite Element Analysis of Structural Systems with Uncertainties. Static Analyses of Truss Structures with Geometrical Uncertainties." Transactions of the Japan Society of Mechanical Engineers Series A 67, no. 662 (2001): 1576–82. http://dx.doi.org/10.1299/kikaia.67.1576.

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IWAYA, Etuo, and Masahide MATSUMOTO. "Interval Finite Element Analysis of Structural Systems with Uncertainties : Static Analyses of Truss Structures with Geometrical Uncertainties." Proceedings of Design & Systems Conference 2001.10 (2001): 223–26. http://dx.doi.org/10.1299/jsmedsd.2001.10.223.

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12

Nesvacil, N., K. Tanderup, R. Pötter, and C. Kirisits. "SP-0504: Impact of geometrical uncertainties in extreme hypo with brachytherapy." Radiotherapy and Oncology 115 (April 2015): S247. http://dx.doi.org/10.1016/s0167-8140(15)40500-6.

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13

Minohara, Shinichi, Masahiro Endo, Tatsuaki Kanai, Hirotoshi Kato, and Hirohiko Tsujii. "Estimating uncertainties of the geometrical range of particle radiotherapy during respiration." International Journal of Radiation Oncology*Biology*Physics 56, no. 1 (May 2003): 121–25. http://dx.doi.org/10.1016/s0360-3016(03)00092-0.

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14

Baysal, Ugur, and B. Murat Eyüboglu. "Tissue resistivity estimation in the presence of positional and geometrical uncertainties." Physics in Medicine and Biology 45, no. 8 (July 25, 2000): 2373–88. http://dx.doi.org/10.1088/0031-9155/45/8/322.

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15

Pivovarov, Dmytro, Kai Willner, and Paul Steinmann. "On spectral fuzzy–stochastic FEM for problems involving polymorphic geometrical uncertainties." Computer Methods in Applied Mechanics and Engineering 350 (June 2019): 432–61. http://dx.doi.org/10.1016/j.cma.2019.02.024.

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16

Lebesque, J. V. "Incorporation of geometrical uncertainties into 3D treatment planning of prostate cancer." Radiotherapy and Oncology 37 (October 1995): S18. http://dx.doi.org/10.1016/0167-8140(96)80501-9.

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17

Zampieri, P., N. Cavalagli, V. Gusella, and C. Pellegrino. "Collapse displacements of masonry arch with geometrical uncertainties on spreading supports." Computers & Structures 208 (October 2018): 118–29. http://dx.doi.org/10.1016/j.compstruc.2018.07.001.

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18

Slavickas, Andrius, Raimondas Pabarčius, Aurimas Tonkūnas, and Eugenijus Ušpuras. "Uncertainty and Sensitivity Analysis of Void Reactivity Feedback for 3D BWR Assembly Model." Science and Technology of Nuclear Installations 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9894727.

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Uncertainty and sensitivity analysis of void reactivity feedback for 3D BWR fuel assembly model is presented in this paper. Uncertainties in basic input data, such as the selection of different cross section library, manufacturing uncertainties in material compositions, and geometrical dimensions, as well as operating data are considered. An extensive modelling of different input data realizations associated with their uncertainties was performed during sensitivity analysis. The propagation of uncertainties was analyzed using the statistical approach. The results revealed that important information on the code predictions can be obtained by analyzing and comparing the codes estimations and their associated uncertainties.
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19

Wunsch, Dirk, and Charles Hirsch. "Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization." Journal of the Global Power and Propulsion Society, May (July 21, 2021): 1–16. http://dx.doi.org/10.33737/jgpps/138902.

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Methodologies to quantify the impact of manufacturing uncertainties in 3D CFD based design strategies have become increasingly available over the past years as well as optimization under uncertainties, aiming at reducing the systems sensitivity to manufacturing uncertainties. This type of non-deterministic simulation depends however strongly on a correct characterization of the manufacturing variability. Experimental data to characterize this variability is not always available or in many cases cannot be sampled in sufficiently high numbers. Principal Component Analysis (PCA) is applied to the sampled geometries and the influence of tolerances classes, sample size and number of retained deformation modes are discussed. It is shown that the geometrical reconstruction accuracy of the deformation modes and reconstruction accuracy of the CFD predictions are not linearly related, which has important implications on the total geometrical variance that needs to be retained. In a second application the characterization of manufacturing uncertainties to a marine propeller is discussed. It is shown that uncertainty quantification and robust design optimization of the marine propeller can successfully be performed on the basis of the derived uncertainties. This leads to a propeller shape that is less sensitive to the manufacturing variability and therefore to a more robust design.
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20

van Herk, M., I. Barrilot, A. Bel, J. Bijhold, A. Bruce, J. de Munck, E. Geeriof, et al. "Imaging processing for evaluation and reduction of geometrical uncertainties in prostate irradiation." European Journal of Cancer 33 (September 1997): S137. http://dx.doi.org/10.1016/s0959-8049(97)85163-5.

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21

Honkanen, J. T. J., M. Loukkola, and J. Seppälä. "EP-2050: Geometrical uncertainties of humerus and breast in breast cancer radiotherapy." Radiotherapy and Oncology 127 (April 2018): S1122. http://dx.doi.org/10.1016/s0167-8140(18)32359-4.

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22

Perel, R. L. "Uncertainties of Responses Calculated with a “Tuned” Library: Geometrical and Algebraic Insights." Journal of ASTM International 9, no. 4 (April 2012): 103992. http://dx.doi.org/10.1520/jai103992.

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23

Schwarz, Marco, Giovanni Mauro Cattaneo, and Livia Marrazzo. "Geometrical and dosimetrical uncertainties in hypofractionated radiotherapy of the lung: A review." Physica Medica 36 (April 2017): 126–39. http://dx.doi.org/10.1016/j.ejmp.2017.02.011.

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24

Antony, Rachitha, Alan Herschtal, Stephen Todd, Claire Phillips, and Annette Haworth. "A pilot study on geometrical uncertainties for intra ocular cancers in radiotherapy." Australasian Physical & Engineering Sciences in Medicine 40, no. 2 (May 2, 2017): 433–39. http://dx.doi.org/10.1007/s13246-017-0551-5.

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25

Silva, Frederico Martins Alves da, Augusta Finotti Brazão, and Paulo Batista Gonçalves. "Influence of Physical and Geometrical Uncertainties in the Parametric Instability Load of an Axially Excited Cylindrical Shell." Mathematical Problems in Engineering 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/758959.

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This work investigates the influence of Young’s modulus, shells thickness, and geometrical imperfection uncertainties on the parametric instability loads of simply supported axially excited cylindrical shells. The Donnell nonlinear shallow shell theory is used for the displacement field of the cylindrical shell and the parameters under investigation are considered as uncertain parameters with a known probability density function in the equilibrium equation. The uncertainties are discretized as Hermite-Chaos polynomials together with the Galerkin stochastic procedure that discretizes the stochastic equation in a set of deterministic equations of motion. Then, a general expression for the transversal displacement is obtained by a perturbation procedure which identifies all nonlinear modes that couple with the linear modes. So, a particular solution is selected which ensures the convergence of the response up to very large deflections. Applying the standard Galerkin method, a discrete system in time domain that considers the uncertainties is obtained and solved by fourth-order Runge-Kutta method. Several numerical strategies are used to study the nonlinear behavior of the shell considering the uncertainties in the parameters. Special attention is given to the influence of the uncertainties on the parametric instability and time response, showing that the Hermite-Chaos polynomial is a good numerical tool.
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26

Jalid, Abdelilah, Said Hariri, and Jean Paul Senelaer. "Estimation of form deviation and the associated uncertainty in coordinate metrology." International Journal of Quality & Reliability Management 32, no. 5 (May 5, 2015): 456–71. http://dx.doi.org/10.1108/ijqrm-06-2012-0087.

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Purpose – The uncertainty evaluation for coordinate measuring machine metrology is problematic due to the diversity of the parameters that can influence the measurement result. From discrete coordinate data taken on curve (or surface) the software of these machines proceeds to an identification of the measured feature, the parameters of the substitute feature serve in the phase of calculation to estimate the form error of form, and the decisions made based on the result measurement may be outliers when the uncertainty associated to the measurement result is not taken into account. The paper aims to discuss these issues. Design/methodology/approach – The authors relied on the orthogonal distance regression (ODR) algorithm to estimate the parameters of the substitute geometrical elements and their uncertainties. The solution of the problem is resolved by an iterative calculation according to the Levenberg Marquard optimization method. The authors have also presented in this paper the propagation model of uncertainties to the circularity error. This model is based on the law of propagation of the uncertainties defined in the GUM. Findings – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and their uncertainties. This contribution allows us to estimate the uncertaintof the form error by applying the law of propagation of uncertainties. An example of calculating the circularity error and the associated uncertainty is explained. This method can be applied to others geometry type: line, plan, sphere, cylinder and cone. Practical implications – This work interested manufacturing firms by allowing them: to meet the normative, which requires that each measurement must be accompanied by its uncertainty-in conformity assessment, the decision-making must take account of this uncertainty to avoid the aberrant decisions. Informing the operators on the capability of their measurement process Originality/value – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and its uncertainties. without the hypothesis of small displacements torsor, this method integrates the uncertainty on the coordinates of points and can be applied in any reference placemark. This contribution allows us also to estimate the uncertainty of the form error by applying the law of propagation of uncertainties.
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27

Stroom, Joep C., Hans C. J. de Boer, Henk Huizenga, and Andries G. Visser. "Inclusion of geometrical uncertainties in radiotherapy treatment planning by means of coverage probability." International Journal of Radiation Oncology*Biology*Physics 43, no. 4 (March 1999): 905–19. http://dx.doi.org/10.1016/s0360-3016(98)00468-4.

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28

Witte, M., J. van der Geer, C. Schneider, J. Lebesque, M. Albert, and M. van Herk. "208 Margin-less prostate IMRT plans, directly optimised for TCP including geometrical uncertainties." Radiotherapy and Oncology 76 (September 2005): S101—S102. http://dx.doi.org/10.1016/s0167-8140(05)81185-5.

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29

Cazaubon, Valentine, Audrey Abi Akle, and Xavier Fischer. "METHOD TO DEFINE MEASUREMENT UNCERTAINTY FOR DESIGN SPACE EXPLORATION IN ADDITIVE MANUFACTURING." Proceedings of the Design Society 1 (July 27, 2021): 2067–76. http://dx.doi.org/10.1017/pds.2021.468.

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AbstractAdditive manufacturing is a process used for quick prototyping in industries. Geometrical defects are observed on printed parts. The aim of the paper is to propose a design method to implement measurements uncertainties into a Design Space for Additive Manufacturing parameters selection. To do so, two tests have been realized. The first test consists in determining the instrument’s uncertainty by measuring a standard length several times by an operator. The second test aim to determine the uncertainty within operators mesurement of geometric outputs (clad’s height, clad’s width, dilution’s height, dilution’s width and contact angle). Based on the results of our tests, uncertainties have been applied in our Design Space populated with 31 real printed clads. The uncertainties display with error bars on scatterplots allow to capitalize the knowledge for his/her exploration of the Design Space for future prints. The given information provides to ease the engineer to select the optimal solution (laser power, tool speed and wire feed speed) for his/her given Additive Manufacturing problematic among candidate points
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30

Zheng, Zhaoli, Qi Jing, Yonghui Xie, and Di Zhang. "An Investigation on the Forced Convection of Al2O3-water Nanofluid Laminar Flow in a Microchannel Under Interval Uncertainties." Applied Sciences 9, no. 3 (January 28, 2019): 432. http://dx.doi.org/10.3390/app9030432.

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Nanofluids are regarded as an effective cooling medium with tremendous potential in heat transfer enhancement. In reality, nanofluids in microchannels are at the mercy of uncertainties unavoidably due to manufacturing error, dispersion of physical properties, and inconstant operating conditions. To obtain a deeper understanding of forced convection of nanofluids in microchannels, uncertainties are suggested to be considered. This paper studies numerically the uncertain forced convection of Al2O3-water nanofluid laminar flow in a grooved microchannel. Uncertainties in material properties and geometrical parameter are considered. The uncertainties are represented by interval variables. By employing Chebyshev polynomial approximation, interval method (IM) is presented to estimate the uncertain thermal performance and flow behavior of the forced convection problem. The validation of the accuracy and effectiveness of IM are demonstrated by a comparison with the scanning method (SM). The variation of temperature, velocity, and Nusselt number are obtained under different interval uncertainties. The results show that the uncertainties have remarkable influences on the simulated thermal performance and flow behavior.
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31

Abdel-Fattah, Mohamed I., and Ahmed Y. Tawfik. "3D Geometric Modeling of the Abu Madi Reservoirs and Its Implication on the Gas Development in Baltim Area (Offshore Nile Delta, Egypt)." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/369143.

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3D geometric modeling has received renewed attention recently, in the context of visual scene understanding. The reservoir geometry of the Baltim fields is described by significant elements, such as thickness, depth maps, and fault planes, resulting from an interpretation based on seismic and well data. Uncertainties affect these elements throughout the entire interpretation process. They have some bearing on the geometric shape and subsequently on the gross reservoir volume (GRV) of the fields. This uncertainty on GRV also impacts volumes of hydrocarbons in place, reserves, and production profiles. Thus, the assessment of geometrical uncertainties is an essential first step in a field study for evaluation, development, and optimization purposes. Seismic data are best integrated with well and reservoir information. A 3D geometric model of the Late Messinian Abu Madi reservoirs in the time and depth domain is used to investigate the influence of the reservoir geometry on the gas entrapment. Important conceptual conclusions about the reservoir system behavior are obtained using this model. The results show that the reservoir shape influences the seismic response of the incised Abu Madi Paleovalley, making it necessary to account for 3D effects in order to obtain accurate results.
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32

Wang, Yuan, and Xiangming Zheng. "Path following of Nano quad-rotors using a novel disturbance observer-enhanced dynamic inversion approach." Aeronautical Journal 123, no. 1266 (June 17, 2019): 1122–34. http://dx.doi.org/10.1017/aer.2019.36.

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ABSTRACTThe model of Nano quad-rotors contains many uncertainties such as an external disturbance from a wind field, highly non-linear strong coupling between variables and body measurement errors. To deal with these uncertainties and control the Nano quad-rotors, a novel data-based disturbance observer (DO) is firstly proposed to observe disturbances from a wind field and perturbations from errors of parameter estimation. Then the DO is used to improve the conventional dynamic inversion (DI) method to obtain an enhanced dynamic inversion (EDI) method, which relies only on roughly estimated geometrical parameters, thus eliminating the largest flaw of conventional DI, namely depending on detailed plant information. Simulation results show that the method proposed achieved good trajectory tracking with only roughly estimated geometrical values under wind field; the DO proposed can accurately estimate disturbance from a wind field and perturbation from error of parameter estimation.
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33

Nakamura, Naoki, Osamu Takahashi, Minobu Kamo, Shogo Hatanaka, Haruna Endo, Norifumi Mizuno, Naoto Shikama, Mami Ogita, and Kenji Sekiguchi. "Effects of Geometrical Uncertainties on Whole Breast Radiotherapy: A Comparison of Four Different Techniques." Journal of Breast Cancer 17, no. 2 (2014): 157. http://dx.doi.org/10.4048/jbc.2014.17.2.157.

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34

Hysing, L., B. Heijman, and L. P. Muren. "SP-0195: PRV margins and other measures to handle geometrical uncertainties of normal tissue." Radiotherapy and Oncology 106 (March 2013): S76. http://dx.doi.org/10.1016/s0167-8140(15)32501-9.

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35

Muren, Ludvig Paul, Randi Ekerold, Yngve Kvinnsland, Àsa Karlsdottir, and Olav Dahl. "On the use of margins for geometrical uncertainties around the rectum in radiotherapy planning." Radiotherapy and Oncology 70, no. 1 (January 2004): 11–19. http://dx.doi.org/10.1016/j.radonc.2003.11.013.

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36

Budiarto, E., M. Keijzer, P. R. Storchi, M. S. Hoogeman, L. Bondar, T. F. Mutanga, H. C. J. de Boer, and A. W. Heemink. "A population-based model to describe geometrical uncertainties in radiotherapy: applied to prostate cases." Physics in Medicine and Biology 56, no. 4 (January 21, 2011): 1045–61. http://dx.doi.org/10.1088/0031-9155/56/4/011.

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37

Nguyen, T. B., A. C. F. Hoole, N. G. Burnet, and S. J. Thomas. "Dose–volume population histogram: a new tool for evaluating plans whilst considering geometrical uncertainties." Physics in Medicine and Biology 54, no. 4 (January 16, 2009): 935–47. http://dx.doi.org/10.1088/0031-9155/54/4/008.

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38

Nakamura, N., O. Takahashi, M. Kamo, S. Hatanaka, H. Endo, N. Shikama, K. Akahane, Y. Tsukada, and K. Sekiguchi. "Effects of Geometrical Uncertainties on Whole Breast Radiation Therapy: Comparison Between 4 Different Techniques." International Journal of Radiation Oncology*Biology*Physics 84, no. 3 (November 2012): S218—S219. http://dx.doi.org/10.1016/j.ijrobp.2012.07.568.

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39

Ali, Salah H. R., and Ihab H. Naeim. "Uncertainty Estimation due to Geometrical Imperfection and Wringing in Calibration of End Standards." ISRN Optics 2013 (September 18, 2013): 1–8. http://dx.doi.org/10.1155/2013/697176.

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Uncertainty in gauge block measurement depends on three major areas, thermal effects, dimension metrology system that includes measurement strategy, and end standard surface perfection grades. In this paper, we focus precisely on estimating the uncertainty due to the geometrical imperfection of measuring surfaces and wringing gab in calibration of end standards grade 0. Optomechanical system equipped with Zygo measurement interferometer (ZMI-1000A) and AFM technique have been employed. A novel protocol of measurement covering the geometric form of end standard surfaces and wrung base platen was experimentally applied. Surface imperfection characteristics of commonly used 6.5 mm GB have been achieved by AFM in 2D and 3D to be applied in three sets of experiments. The results show that there are obvious mapping relations between the geometrical imperfection and wringing thickness of the end standards calibration. Moreover, the predicted uncertainties are clearly estimated within an acceptable range from 0.132 µm, 0.164 µm and 0.202 µm, respectively. Experimental and analytical results are also presented.
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40

Daescu, Dacian N. "On the Deterministic Observation Impact Guidance: A Geometrical Perspective." Monthly Weather Review 137, no. 10 (October 1, 2009): 3567–74. http://dx.doi.org/10.1175/2009mwr2954.1.

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Abstract An optimal use of the atmospheric data in numerical weather prediction requires an objective assessment of the value added by observations to improve the analyses and forecasts of a specific data assimilation system (DAS). This research brings forward the issue of uncertainties in the assessment of observation values based on deterministic observation impact (OBSI) estimations using observing system experiments (OSEs) and the adjoint-DAS framework. The state-to-observation space uncertainty propagation as a result of the errors in the verification state is investigated. For a quadratic forecast error measure, a geometrical perspective is used to provide insight and to convey some of the key aspects of this research. The study is specialized to a DAS implementing a linear analysis scheme and numerical experiments are presented using the Lorenz 40-variable model.
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41

Giunta, G., E. Carrera, and S. Belouettar. "Free Vibration Analysis of Composite Plates via Refined Theories Accounting for Uncertainties." Shock and Vibration 18, no. 4 (2011): 537–54. http://dx.doi.org/10.1155/2011/741801.

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The free vibration analysis of composite thin and relatively thick plates accounting for uncertainty is addressed in this work. Classical and refined two-dimensional models derived via Carrera's Unified Formulation (CUF) are considered. Material properties and geometrical parameters are supposed to be random. The fundamental frequency related to the first bending eigenmode is stochastically described in terms of the mean value, the standard deviation, the related confidence intervals and the cumulative distribution function. The Monte Carlo Method is employed to account for uncertainty. Cross-ply, simply supported, orthotropic plates are accounted for. Symmetric and anti-symmetric lay-ups are investigated. Displacements based and mixed two-dimensional theories are adopted. Equivalent single layer and layer wise approaches are considered. A Navier type solution is assumed. The conducted analyses have shown that for the considered cases, the fundamental natural frequency is not very sensitive to the uncertainty in the material parameters, while uncertainty in the geometrical parameters should be accounted for. In the case of thin plates, all the considered models yield statistically matching results. For relatively thick plates, the difference in the mean value of the natural frequency is due to the different number of degrees of freedom in the model.
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42

Tang, He Sheng, Jiao Wang, Yu Su, and Song Tao Xue. "Evidence Theory for Uncertainty Quantification of Portal Frames with Semi-Rigid Connections." Advanced Materials Research 663 (February 2013): 130–36. http://dx.doi.org/10.4028/www.scientific.net/amr.663.130.

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The buckling load or the equivalent buckling length factor of the portal frame structures is greatly influenced by stiffness of bracing elements and semi-rigid connections. In engineering the problem parameters (geometrical, material, strength, and manufacturing) are given or considered with uncertainties. The initial rotation stiffness uncertainties are taken into consideration. A differential evolution-based computational strategy for the representation of epistemic uncertainty in a system with evidence theory is developed. An uncertainty quantification analysis for the buckling load of portal frames with semi-rigid connections is presented herein to demonstrate accuracy and efficiency of the proposed method.
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43

Silva, Frederico M. A., Paulo B. Gonçalves, and Zenón J. G. N. Del Prado. "Influence of physical and geometrical system parameters uncertainties on the nonlinear oscillations of cylindrical shells." Journal of the Brazilian Society of Mechanical Sciences and Engineering 34, spe2 (2012): 622–32. http://dx.doi.org/10.1590/s1678-58782012000600011.

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44

Van Mourik, A. M. "SP-0503: Impact of geometrical uncertainties in stereotactic radiation therapy: risk assessment and clinical management." Radiotherapy and Oncology 115 (April 2015): S247. http://dx.doi.org/10.1016/s0167-8140(15)40499-2.

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Ferrara, E., D. Beldì, J. Yin, G. Loi, and M. Krengli. "EP-1545 Prostate cancer EBRT: adaptive strategy and use of robust optimization for geometrical uncertainties." Radiotherapy and Oncology 133 (April 2019): S833. http://dx.doi.org/10.1016/s0167-8140(19)31965-6.

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Josipovic, M., M. Aznar, S. Damkjær, J. Thomsen, J. Rydhög, L. Nygård, L. Specht, M. Pøhl, and G. Persson. "EP-2020: Deep inspiration breath hold and locally advanced lung cancer: validation of geometrical uncertainties." Radiotherapy and Oncology 127 (April 2018): S1102—S1103. http://dx.doi.org/10.1016/s0167-8140(18)32329-6.

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Söhn, M. "SP-0441: Treatment planning techniques to handle geometrical and anatomical uncertainties: State of the art." Radiotherapy and Oncology 111 (2014): S175. http://dx.doi.org/10.1016/s0167-8140(15)30546-6.

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Mohammadi, Arash, and Mehrdad Raisee. "Effects of operational and geometrical uncertainties on heat transfer and pressure drop of ribbed passages." Applied Thermal Engineering 125 (October 2017): 686–701. http://dx.doi.org/10.1016/j.applthermaleng.2017.07.047.

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Salehi, Saeed, Mehrdad Raisee, Michel J. Cervantes, and Ahmad Nourbakhsh. "On the flow field and performance of a centrifugal pump under operational and geometrical uncertainties." Applied Mathematical Modelling 61 (September 2018): 540–60. http://dx.doi.org/10.1016/j.apm.2018.05.008.

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Schwarz, Marco, Joris Van der Geer, Marcel Van Herk, Joos V. Lebesque, Ben J. Mijnheer, and Eugène M. F. Damen. "Impact of geometrical uncertainties on 3D CRT and IMRT dose distributions for lung cancer treatment." International Journal of Radiation Oncology*Biology*Physics 65, no. 4 (July 2006): 1260–69. http://dx.doi.org/10.1016/j.ijrobp.2006.03.035.

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