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Littérature scientifique sur le sujet « Sensitivity simulation »

Auteur : Grafiati

Publié le 3 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Sensitivity simulation"

Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear." Advanced Materials Research 268-270 (July 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.

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Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The

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Altieri, S., G. Belli, G. Bruno та ін. "RPC γ sensitivity simulation". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 456, № 1-2 (2000): 99–102. http://dx.doi.org/10.1016/s0168-9002(00)00971-2.

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Brady, Esther C., Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom. "Sensitivity to Glacial Forcing in the CCSM4." Journal of Climate 26, no. 6 (2013): 1901–25. http://dx.doi.org/10.1175/jcli-d-11-00416.1.

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Abstract Results are presented from the Community Climate System Model, version 4 (CCSM4), simulation of the Last Glacial Maximum (LGM) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) at the standard 1° resolution, the same resolution as the majority of the CCSM4 CMIP5 long-term simulations for the historical and future projection scenarios. The forcings and boundary conditions for this simulation follow the protocols of the Paleoclimate Modeling Intercomparison Project, version 3 (PMIP3). Two additional CCSM4 CO2 sensitivity simulations, in which the concentrations are abrup

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Koushik, Priyanka. "Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization." International Journal of Science and Research (IJSR) 13, no. 5 (2024): 875–84. http://dx.doi.org/10.21275/sr24511175813.

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Chen, Chun Jung, Yu Wei Chen, Chang Lung Tsai, Chih Jen Lee, and Jenn Dong Sun. "Incremental Circuit and Sensitivity Simulations Using Iterated Timing Analysis Algorithm." Advanced Materials Research 748 (August 2013): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amr.748.839.

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This paper investigates incremental circuit/sensitivity simulations for large-scale MOSFET circuits using the well-known ITA (Iterated Timing Analysis) algorithm. Incremental simulation uses the result waveforms of previous simulation to fasten the simulation speed, which is quite advantageous in the practical incremental-modifying circuit design strategy. Most proposed methods have been implemented and tested to justify their advantages.

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Li, Zhujun, Paquita Zuidema, Ping Zhu, and Hugh Morrison. "The Sensitivity of Simulated Shallow Cumulus Convection and Cold Pools to Microphysics." Journal of the Atmospheric Sciences 72, no. 9 (2015): 3340–55. http://dx.doi.org/10.1175/jas-d-14-0099.1.

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Abstract The sensitivity of nested WRF simulations of precipitating shallow marine cumuli and cold pools to microphysical parameterization is examined. The simulations differ only in their use of two widely used double-moment rain microphysical schemes: the Thompson and Morrison schemes. Both simulations produce similar mesoscale variability, with the Thompson scheme producing more weak cold pools and the Morrison scheme producing more strong cold pools, which are associated with more intense shallow convection. The most robust difference is that the cloud cover and LWP are significantly large

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Zingale, Michael, Zhi Chen, Melissa Rasmussen, et al. "Sensitivity of Simulations of Double-detonation Type Ia Supernovae to Integration Methodology." Astrophysical Journal 966, no. 2 (2024): 150. http://dx.doi.org/10.3847/1538-4357/ad3441.

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Abstract We study the coupling of hydrodynamics and reactions in simulations of the double-detonation model for Type Ia supernovae. When assessing the convergence of simulations, the focus is usually on spatial resolution; however, the method of coupling the physics together as well as the tolerances used in integrating a reaction network also play an important role. In this paper, we explore how the choices made in both coupling and integrating the reaction portion of a simulation (operator/Strang splitting versus the simplified spectral deferred corrections method we introduced previously) i

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Alagoz, Ekrem. "Numerical Simulation of a Multilateral Saturated Reservoir using MATLAB-based Simulator." Petroleum & Petrochemical Engineering Journal 8, no. 4 (2024): 1–12. https://doi.org/10.23880/ppej-16000399.

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This paper presents a detailed numerical simulation study of a multilateral saturated reservoir using a MATLAB-based simulator. The study focuses on the calculation of various parameters such as pressure, temperature, flow regime, gas and oil velocity, and pressure drops. The simulator uses four MATLAB script files and five function files to perform these calculations. The simulation results are analyzed and presented in various graphs and charts, including pressure vs depth, temperature vs depth, flow regime vs depth, gas and oil velocity vs depth, delta pressures vs depth, mixture density vs

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Chen, Zhi, Eric T. Johnson, Max Katz, Alexander Smith Clark, Brendan Boyd, and Michael Zingale. "A Framework for Exploring Nuclear Physics Sensitivity in Numerical Simulations." Journal of Physics: Conference Series 2742, no. 1 (2024): 012021. http://dx.doi.org/10.1088/1742-6596/2742/1/012021.

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Abstract We describe the AMReX-Astrophysics framework for exploring the sensitivity of astrophysical simulations to the details of a nuclear reaction network, including the number of nuclei, choice of reaction rates, and approximations used. This is explored by modeling a simple detonation with the Castro simulation code. The entire simulation methodology is open-source and GPU-enabled.

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Arteta, J., V. Marécal, and E. D. Rivière. "Regional modelling of tracer transport by tropical convection – Part 2: Sensitivity to model resolutions." Atmospheric Chemistry and Physics Discussions 9, no. 2 (2009): 5929–65. http://dx.doi.org/10.5194/acpd-9-5929-2009.

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Abstract. The general objective of this series of two papers is to evaluate long duration limited-area simulations with idealised tracers as a tool to assess the tracer transport in chemistry-transport models (CTMs). In this second paper we analyse the results of three simulations using different horizontal and vertical resolutions against meteorological observations and their impact on idealized tracer transport. The reference simulation (REF) uses a 60 km horizontal resolution and 300 m vertically in the upper troposphere/lower stratosphere (UTLS). A 20 km horizontal resolution simulation (H

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Thèses sur le sujet "Sensitivity simulation"

Chaban, Habib Fady Ruben. "A numerical sensitivity analysis of streamline simulation." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1541.

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Nowadays, field development strategy has become increasingly dependent on the results of reservoir simulation models. Reservoir studies demand fast and efficient results to make investment decisions that require a reasonable trade off between accuracy and simulation time. One of the suitable options to fulfill this requirement is streamline reservoir simulation technology, which has become very popular in the last few years. Streamline (SL) simulation provides an attractive alternative to conventional reservoir simulation because SL offers high computational efficiency and minimizes numerical

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Kim, Jaehyun 1970. "Causality and sensitivity analysis in distributed design simulation." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8329.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2002.<br>Includes bibliographical references (leaves 109-111).<br>Numerous collaborative design frameworks have been developed to accelerate the product development, and recently environments for building distributed simulations have been proposed. For example, a simulation framework called DOME (Distributed Object-oriented Modeling and Evaluation) has been developed in MIT CADlab. DOME is unique in its decentralized structure that allows heterogeneous simulations to be stitched together while all

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Ekberg, Marie. "Sensitivity analysis of optimization : Examining sensitivity of bottleneck optimization to input data models." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12624.

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The aim of this thesis is to examine optimization sensitivity in SCORE to the accuracy of particular input data models used in a simulation model of a production line. The purpose is to evaluate if it is sufficient to model input data using sample mean and default distributions instead of fitted distributions. An existing production line has been modeled for the simulation study. SCORE is based on maximizing any key performance measure of the production line while simultaneously minimizing the number of improvements necessary to achieve maximum performance. The sensitivity to the input models

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Munukuntla, Sowmya. "Sensitivity Analysis of Synchronous Generators for Real-Time Simulation." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2172.

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The purpose of this thesis is to validate generator models for dynamic studies of power systems using PSS/E (Power System Simulator for Engineering), EMTP (ElectroMagnetic Transient Program), and Hypersim. To thoroughly evaluate the behavior of a power system in the three specified software packages, it is necessary to have an accurate model for the power system, especially the generator which is of interest. The effect of generator modeling on system response under normal conditions and under faulted conditions is investigated in this work. A methodology based on sensitivity analysis of gener

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Liu, Heping Maghsoodloo Saeed. "Taylor Kriging metamodeling for simulation interpolation, sensitivity analysis and optimization." Auburn, Ala., 2009. http://hdl.handle.net/10415/1621.

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Dallaire, Jonathan. "Thermal conductivity of carbon nanotubes from equilibrium molecular dynamics simulations : sensitivity to modeling and simulation parameters." Thesis, Université Laval, 2012. http://www.theses.ulaval.ca/2012/29017/29017.pdf.

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Wang, Jungfeng 1971. "The geometric sensitivity analysis of electrostatic field for microelectromechanical system simulation." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42668.

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GW, Samuelsson John. "Stenotic Flows: Direct Numerical Simulation,Stability and Sensitivity to Asymmetric ShapeVariations." Thesis, KTH, Mekanik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205652.

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Flow through a sinuous stenosis with varying degrees of shape asymmetry andat Reynolds number ranging from 250 up to 800 is investigated using direct numericalsimulation (DNS), global linear stability analysis and sensitivity analysis.The shape asymmetry consists of an offset of the stenosis throat, quantifiedas the eccentricity parameter, E. At low Reynolds numbers in a symmetricgeometry, the flow is steady and symmetric. Our results show that when Reynoldsnumber is increased, the flow obtains two simultaneous linearly stablesteady states through a subcritical Pitchfork bifurcation: a symmetr

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Hirdman, David. "Sensitivity Analysis of the Mesoscale Air Pollution Model TAPM." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303877.

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Known problems with the air pollution model, TAPM’s meteorological predictions over the Canterbury Plains, New Zealand, has been its lack of ability to simulate low nocturnal stagnant wind speeds, certain wind field directions as well as greatly overestimating the sensible heat flux. The aim of this thesis has been to reduce these known disagreements along with other meteorological parameters in attempt to optimize TAPM’s predictions for New Zealand conditions. The methodology used to obtain better agreement with observational data during a four-day period (1-4 of August 2000) was to modify ce

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Sendegeya, Al-Mas. "Simulation of Economical Performance of Isolated Rural Mini-Grids." Licentiate thesis, Stockholm : Skolan för elektro- och systemteknik, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10271.

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Livres sur le sujet "Sensitivity simulation"

D, Roberts Gary, Bowles Kenneth J, and United States. National Aeronautics and Space Administration., eds. ICAN sensitivity analysis. National Aeronautics and Space Administration, 1990.

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Rubinstein, Reuven Y. Monte Carlo optimization, simulation, and sensitivity of queuing networks. Krieger Pub. Co., 1992.

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Rothenberg, Jeff. A " propagative" approach to sensitivity analysis. Rand Corporation, 1990.

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Yucesan, Enver. "An overview of frequency domain methodology for simulation sensitivity analysis". INSEAD, 1986.

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Yucesan, Enver. "An overview of frequency domain methodology for simulation sensitivity analysis". INSEAD, 1986.

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M, Posada J., U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research, Pablo Moreno SA, and Asociación Nuclear Ascó, eds. Study of transients related to AMSAC actuation, sensitivity analysis. Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1998.

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1953-, Saltelli A., ed. Sensitivity analysis in practice: A guide to assessing scientific models. Wiley, 2004.

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1945-, Burns John A., and Langley Research Center, eds. A PDE sensitivity equation for optimal aerodynamic design. National Aeronautics and Space Administration, Langley Research Center, 1996.

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United States. National Aeronautics and Space Administration., ed. Sensitivity analysis and optimization of enclosure radiation with applications to crystal growth: Thesis ... University of Illinois at Urbana-Champaign, Graduate College, 1995.

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Godfrey, P. S. Study of sensitivity of a jacket design to sea current profile. H.M.S.O., 1987.

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Chapitres de livres sur le sujet "Sensitivity simulation"

Belkhode, Pramod, J. P. Modak, V. Vidyasagar, and P. B. Maheshwary. "Sensitivity Analysis." In Mathematical Modeling and Simulation. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132127-8.

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Brauers, Willem K. "Simulation and Sensitivity Analysis." In Nonconvex Optimization and Its Applications. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9178-2_5.

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Pflug, Georg Ch. "Simulation and sensitivity estimation." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1449-3_4.

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Nelson, Barry L., and Linda Pei. "Simulation Optimization and Sensitivity." In International Series in Operations Research & Management Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86194-0_9.

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Wong, Chung F., Tom Thacher, and Herschel Rabitz. "Sensitivity Analysis in Biomolecular Simulation." In Reviews in Computational Chemistry. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470125892.ch6.

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Aicardi, M., F. Davoli, and R. Minciardi. "Approximate Performance and Sensitivity Analysis of Closed Queueing Networks." In Advances in Simulation. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6389-7_62.

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Murray-Smith, David J. "Sensitivity Analysis for Model Evaluation." In Testing and Validation of Computer Simulation Models. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15099-4_4.

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Goldfarb, Alan S., Arlene R. Wusterbarth, Patricia A. Massimini, and Douglas M. Medville. "Sensitivity Analysis Using Discrete Simulation Models." In Cost Estimating and Analysis. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2936-0_16.

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Whitley, Thomas G. "Archaeological Simulation and the Testing Paradigm." In Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27833-9_8.

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Ilievski, Z., H. Xu, A. Verhoeven, E. J. W. ter Maten, W. H. A. Schilders, and R. M. M. Mattheij. "Adjoint Transient Sensitivity Analysis in Circuit Simulation." In Scientific Computing in Electrical Engineering. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71980-9_18.

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Actes de conférences sur le sujet "Sensitivity simulation"

Choy, Keilung, and Wei Xie. "Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838716.

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Zhao, Junkai, Jun Luo, Wei Xie, and Zixuan Bai. "Sensitivity Analysis on Interaction Effects of Policy-Augmented Bayesian Networks." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838820.

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Niewiadomski, Karol, Erjon Ballukja, Piotr Lezynski, and Niek Moonen. "On Sensitivity Analysis Techniques for PE Circuit Simulation." In 2024 International Symposium on Electromagnetic Compatibility – EMC Europe. IEEE, 2024. http://dx.doi.org/10.1109/emceurope59828.2024.10722331.

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Cui, Zhenyu, Kailin Ding, Yanchu Liu, and Lingjiong Zhu. "A New Approach to Sensitivity Analysis Based on Dirac Delta Family Methods." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838623.

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CROBU, Enrico, Simon LANNON, Michael RHODES, and Maria Gabriela ZAPATA. "Simple Simulation Sensitivity Tool." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.1246.

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Glasserman, Paul, and Zongjian Liu. "Sensitivity estimates from characteristic functions." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419689.

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Zhe Yuan, Xianshan Li, Xiangyong Hu, and Qiong Ni. "Trajectory sensitivity analysis based on second-order sensitivity for hydroelectric systems dynamic simulation." In 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing (ICSC). IEEE, 2008. http://dx.doi.org/10.1109/asc-icsc.2008.4675379.

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Lownes, Nicholas, and Randy Machemehl. "Vissim: A Multi-Parameter Sensitivity Analysis." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.323241.

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Sturtevant, John, Peter Buck, Steffen Schulze, et al. "14-nm photomask simulation sensitivity." In 30th European Mask and Lithography Conference, edited by Uwe F. W. Behringer. SPIE, 2014. http://dx.doi.org/10.1117/12.2066483.

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Wang, Yongqiang, Michael C. Fu, and Steven I. Marcus. "Sensitivity analysis for barrier options." In 2009 Winter Simulation Conference - (WSC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wsc.2009.5429560.

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Rapports d'organisations sur le sujet "Sensitivity simulation"

Collis, Samuel Scott, Roscoe Ainsworth Bartlett, Thomas Michael Smith, et al. Sensitivity technologies for large scale simulation. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/921606.

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Vell, J. L., J. E. Oppenlander, W. S. Gaes, D. M. Siganporia, and L. R. Danielson. TPV Network Sensitivity: A Simulation Study. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/822280.

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SAINT-GEOURS, Nathalie, Christian LAVERGNE, Jean-Stéphane BAILLY, and Frédéric GRELOT. Sensitivity analysis of spatial models using geostatistical simulation. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0172.

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Morey, B. E., and D. J. Witte. Sensitivity Analysis of Reflection Errors in Infrared Image Simulation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada208600.

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Magomedov, I. A., and H. A. Murzaev. Sensitivity analysis as a tool for time consumption during simulation. PJSC GAZPROM, 2019. http://dx.doi.org/10.18411/9785-6043-2019-22200.

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Chowdhury, Mostafiz R., and Ala Tabiei. Air Gun Launch Simulation Modeling and Finite Element Model Sensitivity Analysis. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada441366.

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Long, Christopher, Paula Rutherford, Jorden Schulte, Tyler Dean, and Jacob Waltz. Uncertainty Quantification and Sensitivity Analysis for Simulation of Hostile Blast Events. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1894819.

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Picard, Richard Roy, and Kabekode Ghanasham Bhat. Sensitivity Analysis and Uncertainty Quantification for the LAMMPS Molecular Dynamics Simulation Code. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1372820.

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Hsieh, H. Application of the PSUADE tool for Sensitivity Analysis of an Engineering Simulation. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/926408.

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Liou, Lihyeh, David Lin, Ethan Lin, and Chien-In Chen. Sensitivity Simulation of Compressed Sensing Based Electronic Warfare Receiver Using Orthogonal Matching Pursuit Algorithm. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1003317.

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